BM3D Image Denoising using Learning-based Adaptive Hard

Thresholding

Farhan Bashar and Mahmoud R. El-Sakka

Computer Science Department, The University of Western Ontario, London, Ontario, Canada

Keywords:

Image Denoising, Additive White Gaussian Noise, BM3D, Adaptive Threshold, Classiﬁcation, Random

Forest Classiﬁer, PSNR, SSIM.

Abstract:

Block Matching and 3D Filtering (BM3D) is considered to be the current state-of-art algorithm for additive

image denoising. But this algorithm uses a ﬁxed hard threshold value to attenuate noise from a 3D block.

Experiment shows that this ﬁxed hard thresholding deteriorates the performance of BM3D because it does not

consider the context of corresponding blocks. We propose a learning based adaptive hard thresholding method

to solve this problem and found excellent improvement over the original BM3D. Also, BM3D algorithm

requires as an input the value of noise level in the input image. But in real life it is not practical to pass as

an input the noise level of an image to the algorithm. We also added noise level estimation method in our

algorithm without degrading the performance. Experimental results demonstrate that our proposed algorithm

outperforms BM3D in both objective and subjective ﬁdelity criteria.

1 INTRODUCTION

Image denoising is the process of reducing the noise

artifact from a noisy image. This domain of image

processing is very popular and old because images

are often contaminated by different types of noise due

various factors, including the quality of image sensor.

Reducing noise from these images is very important,

as noisy images in different imaging applications can

degrade the performance of that system.

Noise is a random variation of brightness or color

information in images and it can be additive or mul-

tiplicative. Additive noise is independent of image

signal and added to the image. It can be generally

modeled as:

v(x) = u(x) + η(x) (1)

where u(x) is a original signal and η(x) is the noise of

the channel. On the other hand, multiplicative noise

gets multiplied into the image signal. It can be gener-

ally modeled as:

v(x) = u(x) × η(x) (2)

Our study focused on Additive White Gaussian Noise

(AWGN). AWGN refers to the additive noise which

has constant power spectral density and follows a

Gaussian (normal) distribution.

Image denoising can be performed either in the

spatial domain or in the frequency domain. In spa-

cial domain, denoising is done by applying ﬁlter di-

rectly to the intensity values of the image. On the

other hand, in frequency domain techniques, an im-

age is transformed to the frequency domain and then

the ﬁltering operations are performed there, and the

resulting denoised signal is transformed back into the

spatial domain.

From the early stage of image denoising, spatial

domain denoising was very popular because there is

no overhead for domain transformation and it is very

simple. Some of the basic spatial domain ﬁlters are

Mean Filter, Median Filter and Gaussian Smooth-

ing (Gonzalez and Woods, 2008). In these ﬁltering

techniques every pixel is non-adaptively adjudicated

based on the surrounding pixels. It does not take into

account whether the pixel is from a smooth region or

an edge. So edges become blurred in these methods.

To solve this problem Perona and Malik proposed

an edge preserving image denoising technique called

Anisotropic Diffusion (Perona and Malik, 1990). The

main idea of this technique is to ﬁrst identify whether

a pixel is from a smooth region or an edge and then

denoise it.

All of these spatial denoising techniques are pixel-

based denoising schemes. Non-Local Meas (NLM)

changed this idea to patch-based denoising scheme

(Buades et al., 2005). It is the most successful spa-

tial domain image denoising scheme. Instead of ﬁl-

206

Bashar, F. and El-Sakka, M.

BM3D Image Denoising using Learning-based Adaptive Hard Thresholding.

DOI: 10.5220/0005787202040214

In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 3: VISAPP, pages 206-216

ISBN: 978-989-758-175-5

tering a single pixel based on its neighboring pixels,

it works with patches in a particular window. In this

algorithm, a block/patch is deﬁned around a partic-

ular pixel, referred as the reference patch. A search

window is also deﬁned around the pixel where sim-

ilar patches of the reference patch is searched. The

patches are given a weight based on its similarity with

the reference patch. The center pixel of the refer-

ence patch is then denoised by weighted averaging

of all the patch center pixels. A number of improve-

ments has been proposed over NLM algorithm which

have achieved a slight better accuracy over the orig-

inal NLM. Some of the algorithms are: SSIM-Based

Non Local Means (Rehman and Wang, 2011), Adap-

tive Non-Local Means (Thaipanich et al., 2010) and

Non-Local Medians (Chaudhury and Singer, 2012).

In frequency domain denoising, the basic ﬁl-

tering technique is Low Pass Filter (Gonzalez and

Woods, 2008) which allows to pass the signal with

frequencies lower than a cutoff frequency and atten-

uates signals with frequencies higher than the cut-

off frequency. Another popular ﬁlter is Weiner Fil-

ter (Wiener, 1949). It tries to estimate the noise

from a degraded image and denoise it based on this

estimation. The current state-of-art denoising tech-

nique is Block Matching and 3D Filtering (BM3D).

Detailed description of BM3D is described in Sec-

tion 2.1. A number of improvements has been pro-

posed over the BM3D algorithm. Dabov et al. in-

troduced PCA in BM3D and proposed another algo-

rithm, BM3D-SAPCA (Dabov et al., 2009). They

have also extended BM3D for color image denois-

ing (Dabov et al., 2007b) and video denoising (Dabov

et al., 2007a). Dai et al. added adaptive distance hard

thresholding in BM3D (Dai et al., 2013) and Mittal et

al. adapted machine learning based techniques to pre-

dict the noise level parameter used in BM3D (Mittal

et al., 2012). Recently structural similarity is used in

Wiener ﬁltering part of BM3D instead of using tradi-

tional MSE (Hasan and El-Sakka, 2015).

Although BM3D achieves excellent performance

in reducing Additive White Gaussian Noise, it posses

some limitations as well. Our main study focused

on ﬁnding these limitation and provide possible so-

lutions for them. BM3D algorithm relies on a user

provided noise level for each noisy image which is

not possible for real time systems. This noise level

is very important for estimating the denoised image.

We have incorporated a noise level estimation mech-

anism without hampering the performance in this al-

gorithm to convert this as an automated system. Also,

in BM3D a hard thresholding is used for any block of

the noisy image. We have illustrated that this thresh-

olding depends on image block’s texture and noise

level. Tuning this threshold can improve the perfor-

mance of BM3D. Thus we have proposed a learning-

based adaptive hard thresholding mechanism where

each block uses different thresholds based on their

context. From a set of training images, a classiﬁer is

trained by providing the image block as a feature and

their best threshold as a label. From a test image, the

best threshold of its different block is predicted from

this classiﬁer. This best threshold is used in the al-

gorithm to denoise that particular block. Experiments

show that this learning-based adaptive hard threshold-

ing improves the performance of BM3D much over

the original BM3D algorithm.

2 RELATED WORK

In recent years Block Matching and 3D Filtering

(BM3D) becomes the most popular image denoising

technique. Dabov et al. ﬁrst proposed the idea in 2006

(Dabov et al., 2006) and explained it thoroughly in

2007 (Dabov et al., 2007c). BM3D achieves excel-

lent performance in reducing Additive White Gaus-

sian Noise (AWGN). It has achieved the state-of-

the-art denoising performance in terms of both peak

signal-to-noise ratio and subjective visual quality. In

our work we have tried to highlight the limitations of

this algorithm and propose modiﬁcations to improve

the performance of this method.

2.1 Block Matching and 3D Filtering

(BM3D)

BM3D follows the concept of patch-based denois-

ing mechanism where instead of denoising one single

pixel, a patch/block of pixels are denoised at a time,

a concept adapted from the Non-Local Means (NLM)

algorithm (Buades et al., 2005). It was the state-of-

the-art algorithm for image denoising before BM3D,

where a single patch is denoised by ﬁnding its similar

patches from a given window. BM3D also extended

it by denoising an image using two almost identical

steps. In the ﬁrst step, a basic estimate of the noisy

image is generated. This basic estimate is then passed

to the second step to generate the ﬁnal denoised im-

age. The block diagram of this algorithm is shown in

Fig. 1.

2.1.1 BM3D First Step

The ﬁrst step of BM3D is known as the hard thresh-

olding step because a hard thresholding is used to

eliminate noise from the image. First, the noisy image

BM3D Image Denoising using Learning-based Adaptive Hard Thresholding

207

Figure 1: BM3D Block Diagram (Dabov et al., 2007c).

is divided into a number of patches or blocks. A win-

dow is deﬁned centering each patch also referred as

the reference patch and within this window it searches

for the patches similar to the reference patch. As the

initial image is noisy, calculating similarity between

noisy blocks may degrade the performance. So, ﬁrst

the blocks are ﬁltered by using 2D transformation and

then the obtained coefﬁcients are hard thresholded.

Next euclidean-distance between the reference block

and each of the other blocks are calculated. This

similarity measurement is called d-distance. From

this d-distance values, the similar noisy patches are

grouped together into a set, S

. From this set, the

noisy blocks are grouped together in a form of 3D

array which we denote Z

. A 3D linear transform

is applied on this 3D blocks and hard thresholding is

applied on the obtained coefﬁcients, called collabora-

tive ﬁltering. This thresholding attenuates the noise.

An inverse 3D transform is applied to get back to the

spatial domain. Equation (3) shows the block-wise

estimation of a 3D block.

= τ

−1

(ϒ(τ

))) (3)

where ϒ is a hard-threshold operator and

stacked block-wise estimation of noisy blocks in set

. The ﬁnal operation in the ﬁrst step of BM3D is

to aggregate all the estimated blocks together. Each

of the estimated sets contain a number of blocks and

these blocks contain one or more same pixel loca-

tions. That means, a single pixel can have more that

one estimation. So to get the ﬁnal estimation all these

estimations are aggregated together by a weighted av-

eraging method. The basic estimate of the noisy im-

age is then passed to the second step of BM3D to gen-

erate the ﬁnal estimation of the noisy image.

2.1.2 BM3D Second Step

The basic estimated image coming from the ﬁrst step

has signiﬁcantly attenuated noise, compared to the in-

put image. This image is used in the second step as

a reference denoised image of the input image. The

second step of BM3D is identical to the ﬁrst step. At

ﬁrst the similar basic estimated blocks are grouped

together for any reference block. But here, instead

of using the thresholding-based d-distance, a normal-

ized squared l

-distance is used to calculate the sim-

ilarity. The basic estimated blocks and noisy blocks

from input image are stacked. Wiener shrinkage co-

efﬁcients are generated by applying a 3D transform

on the basic estimated group. The 3D transform co-

efﬁcients of noisy blocks are multiplied, element-by-

element, with the Wiener shrinkage coefﬁcients. This

is called Wiener collaborativeﬁltering. An inverse 3D

transform is applied to get the estimated pixel values.

These estimated blocks are then aggregated together

to generate the ﬁnal global estimated image. Aggre-

gation is done by a weighted averaging of the esti-

mated blocks similar to ﬁrst step.

3 MAIN IDEA

In our study and experiments, we tried to overcome

the limitations of BM3D. We focused on two limita-

tions of BM3D which are:

1. Noise Level: In the implementation of BM3D al-

gorithm, we have found that the actual noise vari-

ance of the image is provided to the algorithm

which is used in both ﬁrst and second step. In

real time systems, the actual noise variance is not

practical to be provided as an input.

2. Fixed Hard Thresholding: In the ﬁrst step of

BM3D algorithm, a hard threshold is used to at-

tenuate noise from the 3D blocks. This threshold

is ﬁxed for all the blocks. Using ﬁxed threshold

value deteriorates the performance of this algo-

rithm because for smooth region and textured re-

gion thresholding value should not be same.

We have done several experiments to solve these

two limitations and developed our proposed algo-

rithm. Below is a detailed description of how we

found our solution to these limitations.

VISAPP 2016 - International Conference on Computer Vision Theory and Applications

208

3.1 Automated Noise Estimation

While using the authors’ provided matlab software of

BM3D, we have found that the true noise level (stan-

dard deviation) of input noisy image is provided as in-

put. Also, we have observed that if the noise level is

changed a little, then the performanceof the algorithm

varies a lot. So, we can say that the performance of

the BM3D algorithm provided by the BM3D authors

heavily depends on the value of the input parameter.

In our proposed algorithm, we created an auto-

mated noise estimation system where the noise level

of an input image is calculated ﬁrst. We found that our

noise level estimation is very close to the actual noise

level, hence eliminating such input without degrading

the performance.

For the noise level calculation, we have taken the

idea from the 2D adaptive Wiener ﬁltering (wiener2)

algorithm (Lim, 1990). In this algorithm, image noise

level is estimated based on the local variance of the

image. The local mean and variance around each

pixel are calculated using Equation (4) and (5), re-

spectively

µ(a, b) =

∑

x,y∈η

I(x, y) (4)

and

(a, b) =

∑

x,y∈η

(x, y) − µ

(a, b), (5)

where η is the N×M local neighborhoodof each pixel

in the image I. The noise variance is then calculated

by averaging all the local estimated variance using

Equation (6)

∑

x,y∈η

(x, y), (6)

where σ

(x, y) is the local estimated variance calcu-

lated from Equation (5). From this noise variance,

Equation (6), we can calculate the standard devia-

tion (sigma) of the noisy image. We have experi-

mented using various neighborhood and found that

2 × 2 neighborhood produces the closest estimation

to the actual noise level.

3.2 Context-based Hard Thresholding

In the ﬁrst step of BM3D algorithm, a hard thresh-

olding is applied on the 3D noisy blocks during the

collaborative ﬁltering. This hard thresholding atten-

uates the noise of corresponding blocks; see Section

2.1.

Note that, the hard threshold operator ϒ is ﬁxed

for every blocks of the image.The BM3D authors

used a ﬁxed value, ϒ = 2.7. For image having noise

level greater than 40 they have changed this value to

ϒ = 2.8. This threshold is used to attenuate the noise

of corresponding block. But using a ﬁxed level of

threshold value is not quite a good choice, as blocks

with different properties should have different thresh-

old values.

We started our experiment to ﬁnd out whether dif-

ferent blocks should use different threshold values or

not. First, we took two different images, one with

high texture and the other with smooth region and

then applied same level of noise to both of them. Next

we applied BM3D algorithm on them using various

threshold values and took the best denoised image.

We observed that different threshold values are used

to generate best denoised image in each case. The

experiment is also done using an image with various

noise levels and observed that the best threshold value

also changes with the noise level.

4 PROPOSED METHOD

Our proposed algorithm is divided into two main

parts: training and testing. Detailed description of

the algorithm is explained below.

4.1 Training

In the training phase, we developed 10 different clas-

siﬁer for 10 different noise levels , σ = 10, 20, 30, 40,

50, 60, 70, 80, 90 and 100. Here, image blocks are

used as feature vectors and their corresponding best

threshold value as a label of that vector. Below is a

detailed description for training a single classiﬁer.

4.1.1 Best Threshold Calculation

From the idea of context based hard thresholding, see

Section 3.2, we know that different types of image

blocks should have different threshold values. So, we

conducted experiments on ﬁnding the best threshold

value for any reference block.

1. To build a classiﬁer for noise level n, we have ap-

plied AWGN with σ = n to all input images from

the database.

2. The BM3D image denoising algorithm is applied

to these noisy images generated from the previ-

ous step. During collaborative ﬁltering in the ﬁrst

step, we have used various threshold values for

each block. In our experiments, we have used 22

different threshold values, ranging from 1.7 to 3.8

with step size of 0.1.

BM3D Image Denoising using Learning-based Adaptive Hard Thresholding

209

3. The 22 denoised blocks (based on applying vari-

ous threshold values) are compared with the cor-

responding block in the original true image and

the best among them is selected. The compari-

son is done based on squared euclidean distance

between these two blocks.

4. The threshold corresponding to the best denoised

block is considered to be the best threshold value

of that particular block.

5. Every block and their best threshold value is

stored in a matrix.

After taking all of the best denoised block during

collaborative ﬁltering operation, we found the gener-

ated output to be signiﬁcantly better than the output

of original BM3D algorithm in terms of PSNR and

visual quality. To asses the performance, we have

conducted this experiment and found that the PSNR

is improved by about 3 decimal (on average) when

the best threshold is used, for a given noise level.

4.1.2 Feature Generation

1. For any input image, each of the image block

is considered as a feature vector and their corre-

sponding best threshold value is considered as la-

bel of that feature.

2. Since we are considering blocks of size 7×7, thus

each feature consists of 49 noisy pixel values.

3. In an image of size M × N, a total M × N feature

vectors are generated, each of length 49 features.

4. To reduce memory and time complexities, we

have only taken 10% of the total features, by tak-

ing one feature and leaving the followingnine fea-

tures.

4.1.3 Training Features

1. The generated features and their correspondingla-

bels are used to train a classiﬁer that will be used

in the next part of the algorithm.

In our experiment, we have used different classiﬁca-

tion techniques namely, Naive Bayes, SVM, K Near-

est Neighborhood and Random Forest to ﬁnd out the

best classiﬁer for our algorithm. Based on their per-

formance we have decided to use Random Forest (RF)

classiﬁcation algorithm. Random forests are an en-

semble learning method for classiﬁcation. It is op-

erated by constructing a multitude of decision trees

(classiﬁcation trees) at training time and outputting

the class that is the mode of the classes of the individ-

ual trees. To classify a new object from an input vec-

tor, put the input vector down each of the trees in the

forest. Each tree gives a classiﬁcation which means

the tree votes for that class. The forest chooses the

classiﬁcation having the most votes (over all the trees

in the forest) (Breiman, 2001).

4.2 Testing

In testing step, the classiﬁer developed in the training

part is used to generate the appropriate threshold to be

used to denoise the input image. Detailed description

of the testing phase is given part by part below.

4.2.1 Noise Calculation and Classiﬁer Selection

1. From the test image, initial noise level is calcu-

lated using the algorithm described in Section 3.1.

2. The noise is then rounded to the nearest multiple

of 10. If the value is more than 100, it is clipped

at 100.

3. From the 10 trained classiﬁers, we choose our

classiﬁer based on the noise level.

4.2.2 Feature Vector Generation

1. The noisy input image is divided into several

blocks of size 7 × 7. These 49 noisy pixels are

considered the feature vector of a single block.

2. Feature vectors are generated from all of the

blocks of the noisy image.

4.2.3 Classiﬁcation

1. The feature set is passed to the classiﬁer.

2. The classiﬁer will return the label of each feature

vector.

3. Each of the noisy blocks are then assigned their

corresponding best threshold value.

4.2.4 Output Generation

1. The noisy image is ﬁltered using BM3D image

denoising algorithm with our predicted noise level

where in the collaborative ﬁltering step, adaptive

thresholding is applied.

2. All other operations will remain the same as in the

original BM3D. After the second step of BM3D,

our ﬁnal denoised image is generated.

5 RESULT ANALYSIS

In this section we will compare the performance of

our proposed method with the state-of-art BM3D im-

VISAPP 2016 - International Conference on Computer Vision Theory and Applications

210

(a) Boat2 (b) Lighthouse (c) Woman (d) Sail (e) Statue (f) Model

(g) Beach (h) Bike (i) Bridge (j) Cottage

(k) Door (l) Flower (m) Raft (n) Girl

(o) Hats (p) House2 (q) Houses (r) Windows

(s) Island (t) Lake (u) Landscape (v) Lighthouse2

(w) Parrot (x) Plane

Figure 2: Training Image set (Kodak Image set).

BM3D Image Denoising using Learning-based Adaptive Hard Thresholding

211

(a) Cameraman (b) House (c) Peppers (d) Lena (e) Barbara

(f) Boat (g) Fingerprint (h) Man (i) Couple (j) Hill

Figure 3: Training Image set (Subset of BM3D Test Image set).

age denoising algorithm and the Non-Local Means

(NLM) algorithm.

5.1 Experimental Setup

The performance of our proposed algorithm is con-

ducted on two sets of images: Kodak image set and

BM3D image set. For training the classiﬁers, we

have used Kodak image set which contains 24 differ-

ent grayscale images. For testing, we have taken 10

grayscale images from the BM3D image set. Both of

these set have images containing textured and smooth

regions. The training and test image set is shown in

Fig. 2 and 3, respectively.

5.2 Performance Measurement Metrics

The performance of our experiments are measured us-

ing both subjective and objective ﬁdelity criteria. Ob-

jective ﬁdelity criteria gives us blind results which is

good for understanding the differences between the

outputs and for subjective ﬁdelity criteria, we ob-

served how human eyes perceiving the denoising per-

formance.

For objective ﬁdelity criteria, we have used two

standard measurement metrics. One is Peak Signal

to Noise Ratio (PSNR) which is measured by com-

paring the pixel intensities between original and es-

timated denoised image. The PSNR of an estimated

image ˆy of a true image y, is computed according to

the following standard formula:

PSNR( ˆy) = 10log

255

∑

x∈X

(y(x) − ˆy(x))

(7)

The other metric is The Structural Similarity Image

Measurement (SSIM) technique. SSIM is a measure-

ment metric measuring similarity between two im-

ages (Wang et al., 2004). SSIM is ﬁrst calculated

between patches and the ﬁnal score is given by av-

eraging all the patch scores. The SSIM between two

blocks is calculated using the following formula:

SSIM(x, y) =

(2µ

+ c

)(2σ

x,y

+ c

)

(µ

+ µ

+ c

)(σ

+ σ

+ c2)

(8)

where µ and σ are mean and standard deviation of a

particular block and σ

x,y

is the co-variance between

two blocks.

5.3 Parameters Selection

In our proposed method, the parameters for best win-

dows size (for noise calculation), threshold range and

classiﬁer selection is generated empirically by exper-

iments.

To ﬁnd the best window size in automated noise

estimation step (see Section 3.1), experiment is done

on our training image set. We applied 10 different

noise levels to these images and estimated the noise

level using Equation (6). Different windows sizes are

used to ﬁnd out the best window size value. It is ob-

served that for windowsize, 2×2, the estimated noise

level is almost close to the actual noise applied on

these images. Table 1 shows these results.

In our proposed method, 22 predeﬁned threshold

values are used in the collaborative ﬁltering step of

BM3D. From these threshold values,the best thresh-

old value is used for thresholding a particular block.

We have performed experiments based on different

VISAPP 2016 - International Conference on Computer Vision Theory and Applications

212

Table 1: Comparing estimated noise with true noise based on different window size.

Noise Level

Window Size (η× η)

2 × 2 3× 3 5× 5 7× 7 9× 9 11× 11

10 11.21 14.19 16.79 18.89 20.68 22.25

20 20.15 22.39 24.12 25.63 26.98 28.20

30 30.58 31.64 32.88 34.01 35.04 35.99

40 40.13 41.24 42.20 43.09 43.91 44.67

50 50.24 51.00 51.77 52.50 53.17 53.81

60 60.22 60.83 61.48 62.09 62.67 63.21

70 70.19 70.71 71.27 71.80 72.30 72.77

80 80.18 80.62 81.11 81.57 82.01 82.43

90 90.15 90.55 90.98 91.39 91.79 92.16

100 100.12 100.50 100.88 101.25 101.61 101.95

Table 2: PSNR comparison for different threshold range.

Noise Level

Threshold Range (Total Number of Threshold Values)

2.7 (1) 2.3-3.1 (7) 2.1-3.4 (14) 1.9-3.6 (18) 1.7-3.8 (22) 1.5-4.0 (26)

10 34.45 35.42 35.95 36.39 36.91 36.93

20 31.21 32.27 32.82 33.32 33.95 33.98

30 29.39 30.51 31.08 31.60 32.30 32.33

40 27.95 29.43 30.05 30.58 31.28 31.31

50 27.05 28.12 28.70 29.25 29.97 30.02

60 26.25 27.33 27.91 28.47 29.20 29.25

70 25.56 26.67 27.26 27.81 28.55 28.61

80 24.97 26.10 26.69 27.25 27.99 28.05

90 24.45 25.60 26.19 26.75 27.49 27.56

100 23.98 24.47 25.75 26.30 27.05 27.12

Table 3: Performance comparison using different classiﬁers for testing set (Based on PSNR).

Noise Naive SVM SVM SVM K-NN (k) RF (Number of Trees)

Level Bayes (Linear) (Poly) (RBF) (1) (5) (5) (10) (15)

10 33.25 31.90 33.48 34.25 33.54 34.34 34.42 35.11 35.75

20 29.97 28.26 29.87 31.41 29.96 30.89 31.19 32.03 32.61

30 28.17 26.88 28.40 29.46 28.41 29.25 29.37 30.17 30.84

60 26.75 25.61 27.01 28.08 27.07 28.04 27.94 28.81 29.69

50 25.84 24.67 26.09 26.99 26.12 27.01 27.06 27.90 28.48

60 25.07 23.78 25.17 26.18 25.32 26.12 26.28 27.16 27.68

70 24.42 23.31 24.68 25.50 24.74 25.67 25.59 26.52 27.00

80 23.82 22.89 24.32 24.85 24.33 25.18 25.01 25.96 26.42

90 23.31 22.21 23.62 24.27 23.24 24.19 24.48 25.46 25.91

100 22.88 21.26 22.63 23.65 22.69 23.68 23.90 25.03 25.44

Average 26.35 25.08 26.53 27.46 26.54 27.44 27.52 28.41 28.98

threshold ranges to ﬁnd the range which provides the

best image. Table 2 shows the result of denoised im-

age based on different threshold ranges. It can be ob-

served that if we increase the threshold range, the per-

formance increases. But using 22 and 26 threshold

values produces almost same results. However, tak-

ing 26 threshold values increases the number of class

labels. So, we have taken 22 classes to reduce the

BM3D Image Denoising using Learning-based Adaptive Hard Thresholding

213

time complexity.

Our proposed algorithm requires a classiﬁer to

train. For ﬁnding the best classiﬁer, we have tested

our method using various classiﬁers, such as Naive

Bayes, Support Vector Machine (SVM), K-Nearest

Neighbor and Random Forest. Our experiment shows

that using Random Forests provides the best accuracy

based on PSNR of the denoised image. Table 3 shows

the performance of average denoising result on our

test images.

5.4 Performance Evaluation

The performance of our proposed method is com-

pared with the original BM3D scheme. Also we have

compared with another state-of-art denoising algo-

rithm in spatial domain namely, Non-Local Means

(NLM). Table 4 and 5 shows the average PSNR and

SSIM performance for all test images. The bolded

values represent the best among all these algorithms.

From the results, we can easily observe that our pro-

posed algorithm achieved better performance in both

PSNR and SSIM metrics.

Table 4: PSNR comparison.

Noise Level NLM BM3D Proposed

10 33.10 34.45 35.75

20 29.92 31.21 32.61

30 27.79 29.39 30.84

40 26.23 27.95 29.69

50 25.01 27.05 28.48

60 24.03 26.25 27.68

70 23.21 25.57 27.00

80 22.52 24.97 26.42

90 21.90 24.45 25.91

100 21.35 23.98 25.44

Average 25.51 27.53 28.98

Table 5: SSIM comparison.

Noise Level NLM BM3D Proposed

10 0.895 0.919 0.942

20 0.812 0.865 0.903

30 0.734 0.824 0.875

40 0.660 0.786 0.854

50 0.592 0.759 0.827

60 0.531 0.733 0.807

70 0.478 0.709 0.789

80 0.431 0.687 0.773

90 0.390 0.667 0.758

100 0.355 0.647 0.744

Average 0.590 0.761 0.828

(a) (b)

(e) (f)

Figure 4: Performance Comparison between BM3D and

Proposed Method (a) Original Image (b) Noisy Image

(AWGN added with σ = 100) (c) BM3D (d) Proposed

Method (e) Zoomed BM3D (f) Zoomed Proposed Method.

Fig. 4 shows a subjectuve comparison between

our proposed algorithm and BM3D. We have applied

AWGN noise with σ = 100 on the popular Lena im-

age and denoised it with the original BM3D and our

proposed algorithm. From the subjective viewpoint

the face of Lena is more clear in denoised image pro-

duced using our proposed method, see Fig. 3(e) and

3(f).

5.5 Intensity Proﬁle

Intensity proﬁle is a measure for inspecting how sharp

the edges are after denoising. In an intensity proﬁle,

we choose one scan line on the true image, noisy im-

age and denoised image and plot them together to see

how close the denoised proﬁle is to the original pro-

ﬁle. Also, it shows us how sharp the edges are after

achieving denoising.

VISAPP 2016 - International Conference on Computer Vision Theory and Applications

214

(a)

(b) (c)

(d) (e)

Figure 5: Intensity Proﬁle for Lena Image at scan Line 100 (σ = 50) (a) Lena Image (b) Original Image (c) Noisy Image

(Pearson correlation = 0.5220) (d) Denoised by BM3D (Pearson correlation = 0.9836) (e) Denoised by Proposed Method

(Pearson correlation = 0.9934).

Let us consider the Lena image given in Figure

5(a). Here, we consider the 150

row (indicated by

red line) for our intensity proﬁle calculation. A plot

of this scan line is shown in Figure 5(b). We added

AWGN with σ = 50 to the image and plot that scan

line in Figure 5(c). We can see a lot of noise added

to each of the pixel. Now, to check how close our

proposed method (as well as BM3D) is to the true

noise free image, we perform the same task. That is,

we consider the 150

row from our denoised image

and BM3D’s denoised image and plot them. This is

shown in Figure 5(d) and 5(e) respectively.

It is clear from these plots that our proposed meth-

ods signal is closer to the true signal versus BM3D

counterpart. Also, the Pearson correlation between

the intensity proﬁle of the original image and the pro-

posed method is higher compared to BM3D.

6 CONCLUSION

In this paper, we have proposed a context-based adap-

BM3D Image Denoising using Learning-based Adaptive Hard Thresholding

215

tive hard thresholding to overcomethe shortcoming of

using ﬁxed hard thresholding in BM3D. Experimen-

tal result shows that we have achieved signiﬁcant im-

provement over the original algorithm. However our

algorithm requires training classiﬁers, hence the time

complexity is higher than the original BM3D. Once

the machines are trained, it requires little amount of

time to predict the label of each image block and

hence the time complexity is similar to BM3D. In fu-

ture, we will continue our work to adaptively adjust

other ﬁxed parameters used in BM3D. Also, we will

extend our idea to denoise color image and video se-

quences also.

ACKNOWLEDGEMENTS

This research is partially funded by the Natural Sci-

ences and Engineering Research Council of Canada

(NSERC). This support is greatly appreciated.

REFERENCES

Breiman, L. (2001). Random forests. Machine Learning,

45(1):5–32.

Buades, A., Coll, B., and Morel, J. (2005). A non-local

algorithm for image denoising. In Proc. IEEE Inter-

national Conference on Computer Vision and Pattern

Recognition (CVPR).

Chaudhury, K. N. and Singer, A. (2012). Non-local eu-

clidean medians. IEEE Signal Processing Letters,

19(11):745–748.

Dabov, K., Foi, A., and Egiazarian, K. (2007a). Video de-

noising by sparse 3d transform-domain collaborative

ﬁltering. In Proc. European Signal Processing Con-

ference (EUSIPCO).

Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K.

(2006). Image denoising with block-matching and 3d

ﬁltering. In Proc. SPIE Electronic Imaging.

Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K.

(2007b). Color image denoising via sparse 3d collabo-

rative ﬁltering with grouping constraint in luminance-

chrominance space. In Proc. IEEE International Con-

ference on Image Processing (ICIP).

Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K.

(2007c). Image denoising by sparse 3d transform-

domain collaborative ﬁltering. IEEE Transactions on

Image Processing, 16(8):2080 – 2095.

Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K.

(2009). Bm3d image denoising with shape-adaptive

principal component analysis. In Proc. Workshop on

Signal Processing with Adaptive Sparse Structured

Representations (SPARS).

Dai, L., Zhang, Y., and Li, Y. (2013). Bm3d image denois-

ing algorithm with adaptive distance hard-threshold.

International Journal of Signal Processing, Image

Processing and Pattern Recognition, 6(6):41–50.

Gonzalez, R. C. and Woods, R. E. (2008). Digital Image

Processing. Prentice-Hall Inc.

Hasan, M. and El-Sakka, M. R. (2015). Structural similarity

optimized wiener ﬁlter: A way to ﬁght image noise.

In Proc. International Conference on Image Analysis

and Recognition (ICIAR).

Lim, S. J. (1990). Two-Dimensional Signal and Image Pro-

cessing. Prentice Hall, New Jersey, USA.

Mittal, A., Moorthy, A. K., and Bovik, A. C. (2012). Au-

tomatic parameter prediction for image denoising al-

gorithms using perceptual quality features. In Proc.

SPIE Human Vision and Electronic Imaging.

Perona, P. and Malik, J. (1990). Scale-space and edge de-

tection using anisotropic diffusion. IEEE Transac-

tions on Pattern Analysis and Machine Intelligence,

12(7):629 – 639.

Rehman, A. and Wang, Z. (2011). Ssim-based non-local

means image denoising. In Proc. IEEE International

Conference on Image Processing (ICIP).

Thaipanich, T., Oh, B. T., Wu, P., and Kuo., C. J. (2010).

Adaptive nonlocal means algorithm for image denois-

ing. In Proc. IEEE International Conference on Con-

sumer Electronics (ICCE).

Wang, Z., Bovik, A., Sheikh, H. R., and Simoncelli, E.

(2004). Image quality assessment: from error visi-

bility to structural similarity. IEEE Transactions on

Image Processing, 13(4):600–612.

Wiener, N. (1949). The Interpolation, Extrapolation and

Smoothing of Stationary Time Series. MIT press, New

York.

VISAPP 2016 - International Conference on Computer Vision Theory and Applications

216