Physics-based and Retina-inspired Technique for Image

Enhancement

Mohamed Sedky, Ange A. Malek Aly

and Tomasz Bosakowski

School of Computing, Staffordshire University, Beaconside, Stafford, U.K.

Keywords: Retinex, Image Enhancement, Image Formation Models, Colour Constancy, Contrast Constancy.

Abstract: This paper develops a novel image/video enhancement technique that integrates a physics-based image

formation model, the dichromatic model, with a retina-inspired computational model, multiscale model of

adaptation. In particular, physics-based features (e.g. Power Spectral Distribution of the dominant illuminant

in the scene and the Surface Spectral Reflectance of the objects contained in the image are estimated and are

used as inputs to the multiscale model for adaptation. The results show that our technique can adapt itself to

scene variations such as a change in illumination, scene structure, camera position and shadowing and gives

superior performance over the original model.

1 INTRODUCTION

Image enhancement plays a fundamental role in

computer vision. Images and videos with good

lightness and contrast are a strong requirement in

several areas of applications, where human experts

make an important decision based on the imaging

information, such as medical, security, forensic and

remote sensing applications (Saichandana, 2014).

The objective of an image enhancement algorithm

is to improve the information perception in

images/videos for a human viewer and/or to support

an image/video processing algorithm with a better

input. The result, of such algorithms, is a processed

image that is more suitable than the original image for

a specific application (Maini and Aggarwal, 2010).

Typically, the enhancement is performed to

improve certain desired features e.g. image contrast,

lightness or grey-levels distribution. The choice of

these features and the way they are modified are

specific to a given task.

The well-known methods of image enhancement

include spatial and frequency enhancement

techniques such as linear transformation, gamma

correction, contrast stretching, histogram

equalisation, homomorphic filtering, etc. Those

conventional methods are easy to implement but

generally do not recover exact true colour of the

images hence they have limited application areas.

For example, one of the drawbacks of histogram

equalisation is generating colour distortion in images

during enhancement.

Conventional image enhancement methods have

been widely used with their different advantages and

drawbacks; since the last century, there has been

increased interest in retina-inspired techniques e.g.

Retinex and Cellular Neural Networks (CNN), as

they attempt to mimic the human retina.

Retinex, a compound word comprised of retina

and cortex, is a computational theory derived from

anatomy and neuroscience. Retinex is a very popular

and effective method to remove environmental light

interferences. The two main concepts behind the

theory are: (1) colours of objects are determined by

the capability to reflect rays of light, and (2) colours

are not influenced by uneven illumination. The main

advantages of Retinex are image sharpening and

colour constancy (Land, 1986).

Despite considerable advances in computer vision

techniques, the human eye and visual cortex by far

supersede the performance of state-of-the-art

algorithms.

Marr’s approach to studying computer vision

starts by following what is called representational

theories of minds (Marr and Vision, 1982). He argues

that in order to understand fully a particular machine

carrying out a particular information-processing task,

we have to study that machine as well as the

information-processing task. He saw the

computational study of vision as tightly linked to

psychophysics and neurophysiology. But the last

Sedky, M., Aly, A. and Bosakowski, T.

Physics-based and Retina-inspired Technique for Image Enhancement.

DOI: 10.5220/0005709902390247

In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 4: BIOSIGNALS, pages 239-247

ISBN: 978-989-758-170-0

239

three decades have seen some weakening of that

integration.

Georgeson and Sullivan (Georgeson and Sullivan,

1975) have studied apparent contrast at different

spatial frequencies, in foveal and peripheral vision,

under photopic and scotopic conditions, and at

various orientations in astigmatic subjects. In their

experiments, they found that despite extreme

differences in the contrast thresholds for two patterns,

they will match in apparent contrast when their

physical contrasts are equal. They have concluded

that in man, visual information is blurred by optical

and neural processes restored by an active process of

compensation; they have defined this ability as

‘contrast constancy’.

Similarly, humans have the ability to separate the

illumination power spectral distribution from the

surface spectral reflectance when judging object

appearance, ‘colour constancy’ (Ho et al., 1990).

Image enhancement approaches may be classified

depending on the image representation used as non-

physics-based or physics-based. Non-physics based

image enhancement methods use one of the known

colour spaces as a cue to model the scene. While the

word physics refers to the extraction of intrinsic

features about the materials contained in the scene

based on an understanding of the underlying physics

which govern the image formation. This process is

achieved by applying physics-based image formation

models that attempt to estimate or eliminate the

illumination and/or the geometric parameters to

extract information about the surface spectral

reflectance (SSR) (Sedky, 2014).

Recently, physics-based image formation models

e.g. Dichromatic model, have gained the attention of

the computer vision research community as they

describe mathematically how a digital image is

formed inside a camera, and they attempt to estimate

the Power Spectral Distribution (PSD) of the

dominant illuminant as well as the SSR of objects.

In this paper, we propose a novel physics-based

and retina-inspired image/video enhancement

technique that integrates a physics-based image

formation model, the dichromatic model, with a

retina-inspired computational model, a multiscale

model for adaptation proposed in (Pattanaik et al.,

1998). In particular, physics-based features (e.g. PSD

of the dominant illuminant and the SSR of the objects

contained in the image) are estimated and are used as

inputs to a retina-inspired algorithm to achieve both

colour and contrast constancy to overcome the

limitations of conventional image/video enhancement

methods.

The rest of the paper is organized as follows: the

following Section gives a background on Retina-

inspired models as well as image formation models,

Section 3 describes our computational model, Section

4 presents an illustrative visual comparison between

our proposed technique and the multiscale model for

adaptation, and finally Section 5 concludes the paper.

2 BACKGROUND

Retinex algorithms can be categorised into three

classes, which are: path-based, recursive and

centre/surround Retinex algorithms (Le, 2014).

A. Path-based algorithm: The original work of

Land (Land and McCann, 1971) (Land, 1986)

belongs to this category. In this algorithm, the

lightness of each pixel depends on the multiplication

of ratios along random walks. The drawbacks of this

algorithm are the dependency of the path geometry

and the computational complexity, which has a lot of

parameters.

B. Recursive algorithm: It was developed by

Frankly and McCann (Frankle and McCann, 1983).

This algorithm works on long distance iterations

between pixels, then moves to short distance

interactions. The spacing between pixels being

compared decreases at each step in clockwise order.

This algorithm is computationally more efficient than

the path-based one. The main drawback is that it

depends on the number of times a pixel’s neighbours

are to be visited. This parameter is named some

iterations. The optimal value for this parameter is not

clear and can negatively influence the final result

(Ciurea and Funt, 2004).

C. Centre/Surround algorithm (Morel, 2010): This

technique introduces a weight in the reciprocal

influence of two pixels (Finlayson, 1995), which is

inversely proportional to their square distance.

The lightness values are computed by subtracting

a blurred version of the input image from the original

image. This algorithm is faster than the path-based

one, with less parameter. The main drawback is the

“grey world” assumption, which was addressed

(Berns et al., 1993) by introducing the multi-scale

Retinex with colour restoration technique (Reddy,

2013).

2.1 Image Formation Models

Appearances of scenes depend on four fundamental

elements: an illuminant, a medium, a material and a

vision system. The illuminant represents the source of

visible electromagnetic energy and is characterised

by its PSD. The medium is the medium in which

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240

electromagnetic waves travel. The surface of the

material modulates the incident electromagnetic

energy and is represented by the surface spectral

reflectance, the fraction of incident radiation reflected

by this surface. The vision system is identified by the

spectral sensitivities of its photosensitive sensors that

represent the response of such element to the received

reflected light.

Figure 1: Schematic diagram of image formation.

Another important parameter is the set of

geometrical features, which represent the scene

structure, the illuminant orientation, the surface

roughness and the viewing geometry. These features

combine non-linearly to form a digital image (Sedky,

2014).

Recovering these features from images is an

important problem in image processing; however, this

recovery is generally hard with the limited amount of

information provided by standard commercial

imaging devices.

The Dichromatic Model

The dichromatic model represents the light reflected

by an inhomogeneous dielectric material as a linear

combination of diffuse and specular reflections. Each

of these parts is further divided into two elements, one

accounting for the geometry and another purely

spectral. There are a great number of reflection

models, most of them developed in the field of

computer graphics. Among these methods, the

dichromatic reflection model is a usual choice for

those algorithms employing a physical model to

represent color images, as shown in Figure 1, and the

equation below:



= 

∫  ()  () 

()  + 

∫  () 

() 

(1)

Where Ω is the visible range from 400nm to

700nm, I

is the measured color intensity of the

reflected light, w

and w

are geometrical parameters

for diffuse and specular reflection respectively, E(λ)

is the spectral power distribution function of the

illumination, S(λ) is the SSR of the object, Q

(λ) is the

camera sensor spectral sensitivities characteristic, and

c represents the color channels (Red, Green and

Blue).

Assuming Lambertian surfaces, theoretically, an

image taken by a digital colour camera (for diffuse

only reflection) can be described as:



= 

∫

Ω

 ()  () 

() 

(2)

Surface Spectral Reflectance Estimation

Linear Models

Several researchers (Bajcsy et al., 1990), (Maloney,

1986) and (Marimont and Wandell, 1992) show that

both illumination and surface spectral reflectance are

relatively smooth functions of the wavelength of light

in the visual spectrum and that they can be expressed

using finite-dimensional linear models.

The surface reflectances of a great variety of

materials have been studied. Parkkinnen et al.

(Parkkinen et al., 1989), Maloney (Maloney, 1986)

studied the reflectance properties of the Munsell

chips, which is a database of experimentally

measured surface spectral reflectance characteristics.

Parkkinen concludes that 8 basis functions can cover

almost all existing data in Munsell chips database.

However, it has been shown in the literature (Klinker

et al., 1990) that the spectral reflectance calculated

using the first three basis functions has average error

0.0055 and 0.01. Figure 2 shows the first 4 Parkkinen

basis functions. As it can be seen from the figure, a

significant characteristic of Parkkinen basis functions

is the flat distribution of his first basis function.



(



)

=







()





(3)

where 



() is the i

reflectance linear basis

function, and 



is its corresponding weight,  is the

number of basis functions used.

Using finite-dimensional linear models to

represent the surface spectral reflectance provides a

compact description of data, with few basis functions

we can represent surface spectral reflectance for

general materials. The linear models have been

extensively used in some colour constancy algorithms

where the main aim was to recover either the

illumination or the reflectance functions, or both of

them.

The assumptions made by the dichromatic

reflection model are:

1. There is a single light source that can be a

point source or an area source;

2. The illumination has a constant PSD across

the scene;

Ω

Physics-based and Retina-inspired Technique for Image Enhancement

241

3. The amount of illumination can vary across

the scene.

Figure 2: Parkkinen’s first three basis functions.

For what concerns the surface properties, the

model assumes that:

1. The surface is opaque;

2. The surface is not optically active (no

fluorescence);

3. The colourant is uniformly distributed

The assumption of illumination being due to only

one source of illumination is limiting the application

of such models to scenes where there is a dominant

illuminant. The assumptions about the surface are

typical for reflection models and not too unrealistic.

Illuminant Estimation

The aim of the illuminant estimation is to imitate the

human ability to separate the illumination PSD from

the surface reflectance. Several approaches have been

proposed; this includes methods relying on linear

models (Ho et al., 1990), neural networks (Barnard et

al., 2002), reliance on highlights and mutual

reflection (Klinker et al., 1990), and Bayesian and

probabilistic approaches (Sapiro, 1999).

McCamy et al. (McCamy and Calvin, 1992)

derived a simple equation to compute correlated

colour temperature from CIE 1931 chromaticity

coordinates x and y, which is useful in designing

sources to simulate CIE illuminants.

Vision System

The light emitted by sources of illumination and

modulated by surfaces in the scene arrives at the

capturing sensors of the colour vision system that is

observing the scene. The vision system senses the

captured electromagnetic signal and then transforms

the information carried by light into a colour image of

the physical world.

Colour Charge Coupled Device (CCD) camera,

which is an example of a typical vision system,

contains a set of sensors that convert electromagnetic

energy into electric signals, which are, then sampled

and quantized. Conventional CCD cameras insert

colour filters, with different spectral sensitivity to the

various wavelengths, over each sensory element,

typically red, green, and blue filters to obtain colour

information. Figure 3 shows the spectral sensitivities

of the Sony ICX098BQ (Sony ICX098BQ) CCD

sensors, excluding lens characteristics and light

source characteristics, as an example of a typical

surveillance camera. Apart from the spectral

sensitivity of the colour filters, the formation of

digital image colour values includes other factors,

such as lens characteristics, and the electronics of the

camera.

Once a material surface is hit by a bundle of light

emitted by an illuminant, the electromagnetic waves

may be transmitted, absorbed, or reflected back into

the air. The quantities of transmitted absorbed and

reflected energy sum to the incident energy at each

wavelength. Those quantities are typically measured

in relative terms as a fraction of the incident energy.

Some materials may emit light, or fluorescence effect

may occur, where the material absorbs light at

specific wavelengths and then reflect light at different

wavelengths. The surface’s absorption, transmittance

and reflectance are obtained. In this report, materials

are assumed to be opaque, so light transmission

through the material is not considered. The materials

are assumed not to be fluorescence, and so the

emission is not considered to focus on reflection.

The surface spectral reflectance of a material

refers to the ability of the material to reflect different

spectral distributions when some light shines on it. A

reflectance model is a function that describes the

relationship between incident illumination PSD and

reflected light at a given point on a surface and at each

wavelength. S(λ) is defined as the ratio between the

reflected PSD to the incident PSD. Different

materials have different mechanisms of reflection,

optically; most materials can be divided into two

categories:

1. Homogeneous materials and,

2. Inhomogeneous materials

Homogeneous materials have a uniform refractive

index throughout their surface and bodies.

On the other hand, inhomogeneous materials have

varying refractive index throughout their surfaces and

bodies. If a light hits its surface, part of the light

reflects (specular reflection), while the other part

enters the object and then reflects back to the air

causing diffuse reflection. Such material’s surfaces

are known as diffuse (matte) surfaces or Lambertian

surfaces.

BIOSIGNALS 2016 - 9th International Conference on Bio-inspired Systems and Signal Processing

242

Figure 3: Spectral sensitivity characteristics of the Sony

ICX098BQ camera.

3 COMPUTATIONAL MODEL

Our method can be described in eight stages. The first

stage estimates the SSR based on the dichromatic

colour reflectance model. This approach uses image

formation models to computationally estimate, from

the camera output, a consistent physics-based color

descriptor of the spectral reflectance of surfaces

visible in the image, and then to convert the full-

spectrum reflectance to Long (L), Medium (M), Short

(S) Cone responses and Rod (R) response. This

method represents a new approach to image

enhancement, using explicit hypotheses about the

physics that create images. This step makes our

proposed different from the original model,

(Pattanaik et al., 1998), in that it relies on models,

which can represent wide classes of surface materials.

It makes use of the pre-trained linear SSR models,

shown in Figure 2, to represent the SSR of the objects

in the scene. For what concerns the surface properties,

this model assumes that: the surface is opaque; not

optically active (no fluorescence) and the colourant is

uniformly distributed.

The cones and rod spectral characteristics shown

in Figure 4 are multiplied by the SSR and the result is

integrated to obtain the cone and rod responses, four

images representing the calibrated photoreceptor

responses (L, M, S, R), where they represent, Long,

Medium, Short and rod signals respectively. Those

four signals will be subjected to spatial processing.

The second stage, is the spatial decomposition of

these four images, by applying the Laplacian pyramid

(difference-of-Gaussian pyramid) approach (Burt and

Adelson, 1983). We generate seven Gaussian

pyramid’s levels. Each level of the Gaussian pyramid

represents a low-pass image limited to spatial

frequencies half of those of the next higher level.

The third stage is the gain control stage, where the

difference-of-Gaussian images are then converted to

adapted contrast signals using a luminance gain

control. The gains are set using TVI-like functions

that represent the increment thresholds of the rod and

cone systems and the growth in response required to

allow perceived contrast to increase with luminance

level (sub Weber’s law behaviour). Performing the

gain control at this point in the model allows proper

prediction of chromatic adaptation effects. T

The fourth stage, is the opponent colour

processing, where the model uses the transform of

(Hunt, 1995) to transform the adapted contrast images

for the cones into opponent signals, that will result in

the transformation of the L, M, S-cone signals to A,

, C

, that represent luminance, red-green, and

yellow-blue opponent signals respectively.

The fifth stage is the adapted contrast transducers,

where the adapted contrast signals are then passed

through contrast transducer functions (Watson and

Solomon, 1997). Different transducer functions are

applied to each spatial frequency mechanism to

model psychophysically derived human spatial

contrast sensitivity functions. One of the key

functions of the transducers is to set the threshold

level (200) such that image content that is

imperceptible for a given set of viewing conditions

can be removed.

The sixth stage is the combination of rod and cone

signals, in previous stages it is important to keep the

rod signals separate from the cones to appropriately

integrate their unique adaptation and spatial vision

properties. After the contrast transducers, the rod and

cone signals can be combined to produce signals that

represent the three-dimensional colour appearances

of the input image. At this stage, the model has three

channels representing achromatic, red-green, and

yellow-blue apparent contrast for 6 band-pass

mechanisms.

The seventh stage is the treatment of the low-pass

image, where the lowest level (7

level) low-pass

image from the up-sampled Gaussian pyramid must

be retained in order to reconstruct an image from the

adapted contrast images that have been passed

through the model. The approach used, produces

maximum dynamic range compression, by

multiplying each pixel in the low-pass image by a

gain factor derived from the pixel value itself. This

treatment mimics the visual response assuming that

the observer fixated on each and every image location

and judged them completely independent of one

another.

The eighth and last stage, is the image

reconstruction stage, where the final outputs of the

Physics-based and Retina-inspired Technique for Image Enhancement

243

Figure 5: Illustrative visual comparison of the results obtained, the original image (left), the output of the multiscale model

of adaptation (middle) and physics-based retina-inspired model (right).

model are, signals in an achromatic and two

chromatic channels, six spatial band-pass

mechanisms and a low-pass image. In the last stage,

the model must be inverted, this procedure does not

“undo” the processes of the model since the threshold

and saturated ion procedures are accomplished.

Figure 4: Cones and Rod spectral characteristics.

4 ILLUSTRATIVE VISUAL

COMPARISON

We have implemented our computational model

using Matlab. The results of our implementation, are

evaluated by visually comparing the results of the

multiscale model of adaptation method (Pattanaik et

al., 1998) and our proposed model.

The subjective assessment method was selected to

initially evaluate results obtained by the proposed

physics-based retina-inspired model. This would

allow the authors to identify potential application

where image/video processing algorithms could be

improved by applying the proposed model.

The first image in the top section of Figure 5

shows a view taken from a sea cave looking across a

beach. In this image there is a shadow in the

foreground at the entrance to the cave. The wall of the

cave appears to be saturated almost black, and the

surface details are not defined. The middle image in

the top section is the output obtained by the

application of the multiscale model of adaptation. The

result shows that the model concentrated on

predominantly the darker areas highlighting, in

particular, the shadowed areas. This resulted in an

almost CGI rendition of the original image and

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Figure 6: Illustrative visual comparison of the results obtained, the original image (left), the output of the multiscale model

of adaptation (middle) and physics-based retina-inspired model (right).

appeared unrealistic. When applying our proposed

model (right-hand image top section of Figure 5

however, the definition appears to be more precise

and clearer. On the other hand, the visual model used

in the middle image highlights the details of the sand

patterns within the cave shadow.

The first image of the second row (from the left-

hand side) is of a cloister in a monastic building

highlighting the shadows bouncing off sandstone

columns. The image also defines the details in the

carvings above the pillars. The second image of the

same row is less natural due to oversaturation of

detail. However, the intricate carvings are clearly

highlighted but to some extent exaggerated. The third

image in the same row on this occasion is visually

very similar to the first image.

The bottom left image in Figure 5 shows an

example of a medical image. The output of the

multiscale adaptation model is showing good

improvement by extracting details such as blood

vessels that could improve medical diagnosis.

Although, the results obtained by using our model

(right bottom of Figure 5 shows fewer details, it could

improve the segmentation quality of certain objects,

depending on the required application. The image on

the top of Figure 6 shows an image taken at night. The

gable ends, roofs and building facades are blurred.

The results obtained by the application of multiscale

adaptation model (the top middle image of Figure 6)

shows that too many artefacts were identified, make

it difficult to identify important features such as roof

tops or eaves. In comparison, the top left image of

Figure 6 shows the output of our proposed model.

This brings out the finer details with a reasonable

level of unwanted artefacts. Clear lines of the gable

end, roofs and building facades can be also observed.

The middle left image of Figure 6 represents a

bright image with glare from objects. The multiscale

adaptation model (the second image in the middle

row) although finds more features, it also detects

unwanted artefacts on a desk draw, and this can give

misleading results. It can be also noticed that some

book titles are distorted due to unwanted artefacts.

With the proposed model (middle right of Figure 6)

Physics-based and Retina-inspired Technique for Image Enhancement

245

the output of our algorithm detects less unwanted

data, yet still provides good sharpness of objects.

Finally, the bottom left the image in Figure 6

shows similar scenario as the middle image i.e. a desk

with a lamp and books on one side of the table.

However, this a darker image where the lamp was the

only source of light, and, therefore, the book titles are

not visible. Both models improved the image and

clear book edges are visible, and book titles can be

legible. However, the multiscale adaptation model

(the second image in the last row of Figure 6)

generates unwanted mist effect around the table lamp.

The computational complexity for the proposed

algorithms is evaluated regarding average processing

time for one frame. The presented results are obtained

using a personal computer (processor: Intel i7 3GHz,

Memory: 16GB RAM), an implementation in Matlab

(version: 8.3, release: R2014a). The multiscale model

of adaptation method requires 0.44 sec to process a

240x320 image while our proposed algorithm

requires 1.08 sec.

5 CONCLUSIONS

In this paper, we have developed a novel physics-

based and retina-inspired image/video enhancement

technique that integrates a physics-based image

formation model, the dichromatic model, with a

retina-inspired computational model, a multiscale

model for adaptation.

We have embedded both contrast and colour

constancy by extracting physical features from the

camera output; this approach is unlike other image

enhancement algorithms that use the camera output

directly without considering its physical meaning.

The estimation of the spectral characteristics of

the dominant illuminant allows the proposed

technique to adapt itself to different illumination

conditions; this means that it would be applicable for

a variety of scenes and not to be limited to certain

environments.

Our results have shown that the estimation and use

of physics-based spectral image representations,

deduced from the dichromatic model, represent a

more realistic input to the retina-inspired models and

would mimic the signal received by the human eye.

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