LOCAL BLUR ASSESSMENT IN NATURAL IMAGES

Loreta Adriana Suta

, Mihaela Scuturici

2,3

, Serge Miguet

2,3

, Laure Tougne

2,3

and Mircea-Florin Vaida

Technical University of Cluj Napoca, 400114, Cluj Napoca, Romania

Université de Lyon, CNRS, Bron, France

Université Lyon 2, LIRIS, UMR5205, F-69676, Lyon, France

Keywords: Local Blur Detection, No-reference Blur Metric, Wavelet Analysis.

Abstract: This paper presents a local no-reference blur assessment method in natural macro-like images. The purpose

is to decide the blurriness of the object of interest. In our case, it represents the first step for a plant

recognition system. Blur detection works on small non-overlapping blocks using wavelet decomposition an

edge classification. At the block level the number of edges is less than on global images. A new set of rules

is obtained by a supervised decision tree algorithm trained on a manually labelled base of 1500 blurred/un-

blurred images. Our purpose is to achieve a qualitative decision of the blurriness/sharpness of the object o

interest making it the first step towards a segmentation process. Experimental results show this method

outperforms two other methods found in literature, even if applied on a block basis. Together with a pre-

segmentation step, the method allows to decide if the object of interest (leaf, flower) is sharp in order to

extract precise botanical key identification features (e. g. leaf border).

1 INTRODUCTION

Quality assessment in terms of digital images plays

an important role in various fields, such as image

indexing, segmentation, recognition, etc. Image

quality has received special interest during the last

two decades and a vast number of quality evaluation

indexes have been proposed. Based on the existence

of ground-truth images, these metrics may be

divided into two major classes: full-reference (FR)

and no-reference (NR). However, a third class has

been recently introduced that lies between the two,

namely reduced-reference (RR). This paper deals

with no-reference image quality assessment

designed for blur distortions in macro-like photos of

plants (leaves, flowers) taken in natural scenes. It

serves as the first step towards a pattern recognition

algorithm for plant identification.

One of the most encountered and disturbing

distortion is blur. Blur can affect the entire image, or

parts of it.

Macro mode allows the photographer to take

images from close-up. The focus is on capturing one

This work has been supported by the French National Agency for

Research with the reference ANR-10-CORD-005 (REVES

project).

object, which should be sharp, while the background

remains in blur. Users can shoot a plant image from

close-up in order to recognize its specie. Natural

macro-like images are a combination of edges,

texture details and flat regions, where the color

transitions are almost unnoticeable. This last aspect

limits the use of global quality indexes due to a false

estimation as blur. Figure 1 presents sample images

of leaves containing encountered drawbacks: the

object size, which can be small in comparison with

the blurred background (sometimes taking up to

50% – 70% of the image size) that will mislabel the

image as blurred, or, the background that may

contain the same objects as the one of interest.

Figure 1: Sample blurred images from our database.

The goal is to develop a fast algorithm that finds

whether the intended object is sharp or not.

Although there are various algorithms proposed for a

global quality assessment, one quality index

123

Suta L., Scuturici M., Miguet S., Tougne L. and Vaida M..

LOCAL BLUR ASSESSMENT IN NATURAL IMAGES.

DOI: 10.5220/0003854001230128

In Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP-2012), pages 123-128

ISBN: 978-989-8565-03-7

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

estimated on the entire image is not enough to get a

reliable decision in this case.

In this paper we propose a localized blur

assessment algorithm for natural images inspired by

the previous work of (Tong et al., 2004). Our aim is

to detect the blurred regions that affect the object of

interest, in our case the plant.

The paper is organized as follows: Section 2

describes the existing algorithms for blur

assessment. Section 3 presents the proposed local

blur assessment in natural images. Experimental

results are shown in Section 4 followed by

conclusions and future work in Section 5.

2 RELATED WORK

Blur is a caused by an imperfect image formation

process. There are four types of blur: out-of-focus,

camera shake, object motion and atmospheric blur

(fog, rain, etc.). In this section, we restrained our

research towards objective no-reference blur metrics,

since we do not dispose of the reference image.

Objective no-reference blur detection methods

address two types of evaluation introducing global

and local metrics, respectively. Global assessment

reveals blur extent coefficients, blur classification

and restoration possibilities. However, methods

work successfully over landscape images, these

indexes have limitations when applied to macro-like

photos. Local metrics are often combined with the

pre-use of a global evaluation or a block division of

the original image. It is more intuitive, as avoids

mistakenly focusing on the blurry background.

Multiple approaches to blur detection are based

on edge detection (Tong et al., 2004), (Chen and

Bovik, 2011), (Narvekar and Karam, 2011)]. (Tong

et al., 2004) propose a blur detection scheme built on

the Haar wavelet transform and edge detection. The

algorithm is based on the estimation of edge

sharpness and the computation of the blur extent

based on a discrimination of sharp edges. The results

show a global assessment of blurred landscape

images. For macro-type images, the algorithm fails.

(Moorthy and Bovik, 2010) and (Chen and Bovik,

2011) investigate a blur metric using wavelets for

natural scenes. The first approach represents a blind

assessment based on a probabilistic support vector

machine (SVM) and a support vector regression

(SVR) in order to map the image statistics in one

global value. The second approach consists of three

steps. At first, the SVM is applied to get a coarse

quality assessment. It is followed by a multi-

resolution analysis to refine the blur metric and the

last step consists of the prediction of the blur metric.

In (Narvekar and Karam, 2011), a no-reference blur

metric method is described based on the human

perception and edge analysis. It is a probabilistic

method applied on each edge in an image arriving to

a global assessment of Gaussian and JPEG2000 blur

types, respectively.

The estimation of the blur kernel is commonly

used to detect and classify blurred images (Joshi et

al., 2008), (Hsu and Chen, 2008). Linear blur in

digital images is described using a “blur kernel” or

the point-spread-function (PSF). The drawback is

that linear blur does not include all blur types (e. g.

out-of-focus blur). In (Joshi et al., 2008), a blur

detection algorithm for spatially-varying blur

functions is presented, in particular the estimation of

point-spread function (PSF). The method handles

defocus blur, camera motion and intrinsic image

formation. The method is completely automatic and

scene-independent. (Hsu and Chen, 2008), propose a

blurred image detection and classification algorithm

based on the estimation of point spread function and

the use of support vector machine (SVM). The blur

extent is computed using the image gradient model.

Next, images are classified into globally or locally

blurred images using the point spread function.

Furthermore, globally blurred images are sorted into

camera shake or out-of-focus, while on locally

blurred images a segmentation algorithm is

performed to detect the blurred regions and classify

them into depth-of-field or moving object type,

respectively. The use of SVM increases

computational costs and complexity.

A region-based blur detection approach has been

conducted by (Lim et al., 2005). Images are divided

into non-overlapping blocks where local measures

are computed through image analysis. Based on the

figure-of-merits, the estimated parameters are

brightness, color, median sharpness, density of sharp

blocks and composition. The success rate is 90%,

while the algorithm produces 10% of false alarms.

Global detection works well over landscape

images where blur is usually linear. Complex blur

kernels are difficult to estimate using only global

quality metrics. Determining a probability of rather

sharp/blurry image is not enough to decide whether

the image may or may not be used in further

processing steps. Localization of blurred regions are

more adequate to solve problems related to objects

of interest, which will consist the input for

segmentation algorithms, feature extraction or

recognition. The low computational cost of the Haar

wavelet analysis suits the scope of implementation

on smartphones.

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3 METHODOLOGY

Multi-resolution analysis has been proven to provide

reliable results in blur assessment tasks. The spatial-

spectral properties may identify important changes

in the high-frequency coefficients that correspond to

the edges in the digital photo. According to (Tong et

al., 2004), there are four types of edges found in

digital images: Dirac-Structure, Roof-Structure

Astep-Structure and Gstep-Structure, respectively.

Previous work of Tong provides a direct method

for blur assessment without the description of the

blur kernel. The method shows good results for

landscape images. Applied to macro-like photos, it

reliably detects camera-shake blur or out-of-focus if

the entire image is affected. The drawback remains

for partially blurred images exhibiting object motion

blur or blurred background.

Out-of-focus blur introduces a separation

between two image planes, the background and the

foreground, leading to the following situations

where a global detection method is not reliable:

• The background is blurred and the

foreground is sharp. The size of the sharp

foreground may be too small with respect

to the size of the image.

• The foreground is blurred and the

background is sharp.

Another drawback of the existing global methods

appears when the size of the sharp foreground is too

big and presents uniform color zones. Figure 1

shows examples for the above mentioned cases.

3.1 Proposed Method

We propose a local blur detection technique using

three-level multi-resolution analysis designed for

macro-like photos. Figure 2 presents the

computational block diagram for the proposed

method.

3.1.1 Block Selection

One user-taken plant image, , is divided into

smaller non-overlapping image blocks, 



. The

choice of block dimension is an essential step as it

directly affects the accuracy. As the wavelet

decomposition is dyadic, the best adapted block size

is 2



The pre-segmented model, 



, allows us to find

the approximate localization of the rough contour of

the object (Region of Interest, ROI). Its computation

uses a 2-component Gaussian mixture model and the

Mahalanobis distance, (Cerutti et al., 2011).

Figure 2: Computational block diagram for the proposed

method.

Figure 3: The distance map (left – original image, middle -

2-Gaussians color map, right – pre-segmented model).

Figure 3 illustrates the obtained distance map

based on which the appropriate pre-segmented

model is selected.





is divided into blocks with the same size as

previously applied on  to have a perfect

correspondence between the two inputs.

The following operations are executed on 



Firstly, a decision on the existence of an object

contour is computed. To avoid blocks with

ambiguous information, as containing only an

insignificant amount of the object, two thresholds

have been set over the total number of black and

white pixels respectively, found in the model block.

If the values are under the accepted thresholds, 



rejected from further processing steps.

3.1.2 Local Blur Detection

Each valid block is decomposed by the wavelet

transform, resulting in three sets of detail

coefficients on every decomposition level.

Edge type detection and analysis is performed as

in (Tong et al., 2004), obtaining the statistical

parameters for each edge type. The obtained detail

LOCAL BLUR ASSESSMENT IN NATURAL IMAGES

125

coefficients are used to calculate the energy map as

in (1):









,















,1,2,3 (1)

where dc suggests the detail coefficients for each

decomposition level given by i.

Based on (1) and using the table given in (Tong

et al., 2004), we can identify and compute the total

number for each of the four edge types.

Contrary to the method of Tong, the decision

thresholds are completely omitted due to the low

edge information and uniform color regions in

macro photos. We computed a new set of decision

rules with supervised learning method using a

decision-tree based on the C4.5 algorithm. 1504

block images have been manually labeled into two

categories, as follows: “blurred” and “unblurred”.

Together with the color features, these were

involved in the training of the decision tree. The

most relevant decision rules for 



, obtained using

the method C4.5, are presented in Figure 4.

Figure 4: Decision tree presenting the set of rules.

where Nedge represents the total number of edges,

Nda is the number of Dirac and A-Step classified

edges, and Nbrg describes the number of blurred

Roof-Step and G-Step edges.

The error rate in cross-validation is 0.01.

Table 1 shows the confusion matrix in cross-

validation.

Table 1: Confusion matrix on trained data set.

Groundtruth

Decision

Blurred Sharp Total

Blurred 495 10 505

Sharp 5 994 999

Total 500 1004 1504

The rules given by the supervised learning

have been involved in the threshold and decision

step. Based on the set of rules, 



is labeled as

blurred or not blurred, respectively. Figure 5 shows

the block separation and labelling as “B” for blurred

and “OK” for un-blurred image blocks. Two macro

images were analysed exhibiting sharp and blurred

foreground, respectively, where global metrics give

“blurred” decision.

Figure 5: Blurred blocks labeling based on the set of rules

(left – sharp foreground; right – blurred foreground).

We can compute a global blur metric based on

the existent number of blurred blocks given by (2).

 









(2)

where 



represents the number of blurred

blocks and 



represents the total number of

blocks of the image.

4 EXPERIMENTAL RESULTS

Tests were performed on two databases: LIVE

database (174 images), (Sheikh, Wang, Cormack,

and Bovik), and our ReVeS databases (94 images).

ReVeS images consist of a collection of

unprofessional user-taken photos using a smartphone

camera. Each photo represents one or more parts of

a plant in its natural habitat. 1504 non-overlapping

block images were manually labeled into two

categories: blurred and non-blurred.

On the previously described databases we

performed our method and the objective evaluation

using the cumulative probability blur detection,

CPBD (Narvekar and Karam, 2011), and blind

image quality index, BIQI (Moorthy and Bovik,

2010). These methods offer global metrics. For

comparison, we computed the global blur parameter

for each photo given by (2).

Table 2 presents test results obtained on the

analyzed databases for each algorithm. The last

column in Table 2 presents the Spearman rank order

correlation coefficient (SROCC) between the blur

coefficient of the CPBD algorithm and our blur

coefficient. We can observe a moderately strong

positive correlation of 0.74 between our results and

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126

the CPBD for the test performed on entire images.

On the contrary, tests conducted over ReVeS

database show a negative correlation which is

explained by a high quantity of images with out-of-

focus blur (small sharp object surrounded by a

blurred background) or images containing motion

blur which is not detected by the CPBD algorithm.

Table 2 : Test results on different databases.

DB DB size Algorithm SROCC

LIVE 174 CPBD 0.74

LIVE 174 BIQI 0.87

ReVeS 94 CPBD -0.85

ReVeS 94 BIQI 0.73

Figure 6: ReVeS database images (left – Out-of-focus

blur; right – camera-shake blur).

Figure 6 highlights common problems

encountered in macro-like images taken by users

with smartphone cameras. There are two frequent

blur types that degrades the image quality, out-of-

focus blur affecting the object of interest and

camera-shake blur, respectively. An objective

evaluation using the CPBD metric predicts rather

sharpness on both images. The computed values are

0.85 and 0.77, where the maximum of 1.00 stands

for “sharp image”. However, our proposed method

successfully detects the degradations with a blur

estimation of 0.99 and 0.98, where the maximum of

1.00 designs a “blurred image”.

The local assessment has been performed on

non-overlapping image blocks. The images from

ReVeS database have been divided into blocks as

described in the previous section and stored as JPG

images with the size of 2



2



. Tests revealed that

in order to be able to identify all four edge types on

the three decomposition levels, the minimum block

size must be 2



2



. Table 3 shows the influence of

size on the edge detection.

Table 3: Influence of block size on edge detection.

Block Size Detected Edge Types

1616 No edges

3232 Non-discernible edge types

6464 Ambiguous parameters

128128 OK

Figure 7: Graphical representation of precision-recall over

the manually labeled image blocks from ReVeS database.

Figure 7 presents the precision and recall (ROC

curve) for the three algorithms applied on the image

blocks generated from the ReVeS database. The red

point represents the precision/recall of our

algorithm. The result of our algorithm is a

qualitative decision (blurred/unblurred) given by the

decision tree presented in Figure 4. For the two other

methods, in order to compare them with our method,

we vary the threshold allowing a decision as

blurred/unblurred according to the coefficients

calculated in their algorithms – in order to take the

best value for this threshold. We vary the threshold

by a 0.1 step. The ideal point in the ROC curve is

(1,1): precision = 1 and recall = 1. Our algorithm

gives the best results (0.984, 0.998), even for the

best threshold values for each of the CPBD and

BIQI algorithms.

As our interest is the contour of a shape, we use

the pre-segmented model to find the approximate

region of analysis. Figure 8 illustrates results

obtained by our method. Note that missing blocks

over the leaf object are due to the thresholds

imposed on the pre-segmented model for the amount

of black and white pixels distribution.

Figure 8: Test result using the proposed method (first row

from left to right – original image and the distance map;

second row from left to right – pre-segmented model and

algorithm output).

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127

5 CONCLUSIONS

In this paper we presented a no-reference local blur

assessment method for macro-like images. Contrary

to other blur detection methods, the proposed

algorithm can localize blurred regions over the

image and with the computed model, the estimation

can be done over the region of interest.

The proposed method uses wavelet analysis for

edge detection and classification. The obtained

parameters are adjusted by using a supervised

decision tree algorithm trained on a manually

labeled base of 1504 blurred/un-blurred images

According to the difficulty of achieving a

qualitative blur decision based on a single

quantitative value, the set of rules given by the

decision tree let us partition an image into blurred

and sharp regions while the pre-segmentation model

localizes the rough position of the object of interest.

The use of the pre-segmented model also reduces the

computational costs.

Future work includes studying the possibility of

a separation between foreground and background

using the proposed algorithm. The REVES project

had proposed to include the quality detection as a

first step of a complete processing chain. These first

results suggest that segmentation and quality

assessment should rather cooperate: the

segmentation can help blur detection, the blur

estimation process can also help the identification of

different planes in the scene.

ACKNOWLEDGEMENTS

This work has been supported by the French

National Agency for Research with the reference

ANR-10-CORD-005 (REVES project) and co-

financed from SIDOC - POSDRU/88/1.5/S/60078

project.

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