Proceedings of the Second APSIPA Annual Summit and Conference, pages 310–313,Biopolis, Singapore, 14-17 December 2010.

Two-layer Image Contrast Enhancement inPerceptual DomainZhongkang Lu∗ and Susanto Rahardja∗

∗ Institute for Infocomm Research, Agency for Science, Technology and Research1 Fusionopolis Way, Singapore 138632

Email: {zklu, rsusanto}@i2r.a-star.edu.sg

Abstract—Image contrast enhancement is an old but stillattractive topic in image processing applications. Most operatorsare capable of producing visually pleasing results by interactiveadjustment. However, most interactive based methods need tojointly manipulate multiple parameters to achieve a distortion-free and visually pleasing output, which require a large numberof trials or a certain level of knowledge/experience. In this paper,we present a new algorithm to simplify the image contrastenhancement interactivity. A contrast perception model, whichunifies human contrast perception characteristics with four visualfactors, is adapted in the algorithm. The four factors includephysical contrast and frequency of the contrast signal, thebackground brightness, and a distance value that indicates howclose the signal is to eye-gaze point, or visual eccentricity. Thecoefficients of the image enhancement are assigned based on thecontrast perception model and a perceptual enhancement levelparameter. Users can adjust the single perceptual enhancementvalue to achieve realistic image enhancement results. Experimen-tal results demonstrated the performance of the proposed imagecontrast enhancement algorithm.

I. INTRODUCTION

For most image enhancement applications, the aim is toimprove the interpretability or perception of information inimages for human viewers. Contrast is one of the mostimportant features to human eyes. They reflect the boundaryand surface characteristics of objects, and play an importantrole in distinguishing one object from others. Owing to thisimportance, many image contrast enhancement algorithmshave been developed in the last fifty years. Early imagecontrast enhancement algorithms can be divided into two cate-gories: spatial methods and frequency-based methods. Spatialmethods enhance image contrast in spatial domain. Amongthe existing methods, histogram equalization/adjustment is themost popular one. The technique adjust grayscale transformcurve to stretch image contrast. Frequency-based methodsgenerally operate on the Fourier transform of an image, andhigh frequency components are normally raised to improveimage sharpness. However, spatial methods always cannotprovide enough enhancement on image details, and frequency-based methods always bring visual artifacts, such as halo andringing effect.

Multiscale methods draw a lot of attention in recent years.These techniques split an image into a number of channelsby spatial scale, frequency and orientation, etc. Image en-hancement is realized by amplifying certain channels [1],[2]. The decomposition techniques include Laplacian Pyramid,

Gabor filters, Wavelet and Curvelet transforms, etc. Recently,edge-preserving and bilateral filters are developed for imagedecomposition [3], [4], [5]. The decomposition is based onboth the gradient and scale properties of an image, and avoidshaving pixels from both sides of an edge. This allows thestrong edge contents that essentially represents object bound-ary to be separated from object textures. The separation helpsto improve the enhancement performance because users canapply different amplifying strategies on each decompositionchannel. Using correct amplifying coefficients, it is capableof generating very clear images with minimal visual artifacts.However, the assignments of amplifying coefficients are highlyimage content dependent, and interactive adjustment on localcoefficients is always required to achieve a visually pleasingimage. The complex interactivity significantly limits the us-ability of these applications.

In this paper, a two-layer image contrast enhancementalgorithm is developed. Using Farbman’s edge-preserving de-composition filter [5], the input image is separated into twolayers: base layer and detail layer. The base layer describesthe global illumination, or global tone; and the detail layerrepresents object textures. A further fine-tuned unified contrastperception model is used to measure the perceptual responseof image contrasts. The enhancement on detail layer can beregarded as a lifting of perceptual response on the details,which represents as a local scale-up. A different enhancementstrategy is applied to the base layer, which only contains strongedges. Based on the simplified contrast perceptual model, theedges are stretched to improve their sharpness. The outputimage is a combination of processed base and detail layer,and only one perceptual parameter is applied to control theenhancement level, the interactivity is greatly simplified.

The paper is organized as following: Section II introducesthe details of the image contrast enhancement algorithm; Theexperimental results are presented in Section III; and theconclusions are given in Section IV.

II. IMAGE CONTRAST ENHANCEMENT ALGORITHM

In the proposed algorithm, different enhancement strategiesare applied to the base layer and the detail layers. Both utilizethe unified contrast perception model, as shown in figure 1.

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Fig. 1. Diagram of the proposed image contrast enhancement algorithm.

A. Unified contrast perception model

The unified contrast perception model was previously intro-duced in [6], [7] for High Dynamic Range (HDR) image tone-mapping. In [6], the contrast perception model was presentedin a Low Dynamic Range (LDR) display brightness indexdomain. The HDR tone-mapping is realized by matching theperceptual response from the input HDR and output LDRimages. Plainis model [8] was adopted to estimate the HDRperceptual response. However, acceptable tone-mapped imageonly can be obtained by raising and compressing the results ofPlainis model many times, which the authors cannot explain.In our later paper [7], which is a brief presentation, subjectiveviewing results are extended to HDR range and an HDRcontrast perception model is built. In this paper, the unifiedcontrast perception model is further fine-tuned into a differentformula. Moreover, 8-bit image format is still widely used incurrent imaging industry, a display model is used to convert thecontrast perception model from real-world luminance domaininto 256 grayscale brightness domain. A 24-inch LCD monitorunder a normal office lighting environment is used as areference display model. It has a γ = 2.2 gamma correctioncurve and 1920× 1200 resolution. The maximal and minimalluminance values of the monitor are 300 and 1 cd/m2,respectively. And the ambient reflection is about 1 cd/m2.Using the display model, the contrast perception model canexpressed as following:

RT = fRT (C, F,Bind, D)

= a1 · ea2·C+a3·F+a4·D+a5·Bind + a6 (1)

where RT denotes the reaction time of the contrast stimulusin human visual system. {ai : i = 1, · · · , 6} are modelparameters. C = Dmax − Dmin represents local physicalcontrast, where Dmax and Dmin denote local maximal andminimal values in detail layer. F , Bind and D denote theother three visual factors: frequency, background brightnessindex and the angle distance from stimulus to eye-gaze whenit appears (or eccentricity), respectively. F is estimated usingwavelet transform, and the local dominant frequency value isselected. Moreover, we have Bind = |B − 127| and B is theaverage of local base layer. It should be noted that a fixeddistance D is used in the operator to improve computational

efficiency. In current implementation, a value suitably betweenfoveal and peripheral vision is assigned to D.

It is assumed that contrast perceptual response PR has thefollowing relationship to reaction time RT :

PR = − log(RT + C0) (2)

where C0 = 0 currently.When physical contrast C is high, PR has a almost linear

relationship to C, which can be represented as:

PR ≈ A · C + B (3)

where A and B are constants.It should be noted that the only luminance contrast is used in

the unified contrast perception model, the proposed algorithmshould be applied to grayscale image enhancement.

B. Detail layer lifting

The details lifting is realized by increasing the PR value,or shortening the reaction time RT of the local details. LetP ′R denotes the perceptual response value after lifting, thenwe have following lifting scheme:

P ′R − β

PR − β= 1− α (4)

where β is a pre-defined parameter which represents a strongperceptual local detail, which is invariant to backgroundbrightness and local frequency. 0 < α < 1 is the valuethat indicates the perceptual enhancement level. The mappingfunction means the strong details should be maintained inoutput image and the weak details should be lifted to improvetheir perceivability. The weaker the detail is, the stronger itwill be enhanced. For example, when the perceptual responsevalue of an input detail is around β, no lifting is applied. Onthe contrary, given an input weak detail with PR = 0, thenwe have P ′R = α · β, which means the perceptual response isenhanced by α·β, or the reaction time of the detail is shortenedby eα·β times. α = 0 implies no enhancement is applied.

Because Equation 1 is a monotonic equations to C, andEquation 2 is a monotonic equations to RT too, we canestimate a C ′ from P ′R. As a result, a local scale factor canbe obtained by:

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Fig. 2. A paradigm of base layer stretching.

S =C ′

C(5)

Obviously, S is adaptive to image contents. The enhanceddetail layer can be obtained by multiplying with the scalefactors. Other lifting schemes, such as nonlinear lifting andequalization, can also be used. However, experiments andobservations showed the linear lifting scheme always givesa better result. It is believed linear lifting maintains therelationship among the details in whole image. The strongdetail in input is strong in output, and the weak is stillcomparatively weak in output. Keeping the relationship helpsto maintain a natural look of the enhanced image.

C. Base layer stretching

As mentioned before, it is assumed that base layer repre-sents global tone, which also should be enhanced. Consideringthat base layer mostly contains strong edges and halo alwaysappear around after they are enhanced, a different scheme isdeveloped. With a reference of Equation 3, it can be assumedthat the contrasts in base layer has a linear relationshipto perceptual response. They need to be stretched by theperceptual factor α. A Unsharp Masking (USM) operator isadapted. The USM operator uses Gaussian core with a bigradius value to prevent halo. The difference between the baselayer and its Gaussian blurred copy is multiplied by α, andthen added to the original base layer. Using the USM operatorcan enhance the edges, also also avoiding stretch the base layertoo much, as shown in Figure 2.

A value that is equivalent to 0.25 visual degree should beassigned to the σ parameter of the Gaussian core. Based onthe observation that viewers’ eyes generally are at a distanceabout 1.5× of monitor height in a normal image viewingenvironment. A σ = 16 pixel is used in current implementationwith a reference of the above-mentioned monitor model.

III. EXPERIMENTAL RESULTS

Figure 3 shows the experimental results on “Lena” image.Figure 3(a) is the input grayscale image. Figure 3(b)-(d) arethe enhanced images with α = 0.2, α = 0.5 and α = 0.8,respectively. With the increase of α value, the image is en-hanced in a perceptually stronger way. Moreover, Figure 3(e)shows the base layer, which is separated by the decompositionfilter. And Figure 3(d) is the enhanced base layer. Because wedidn’t consider noise in the proposed algorithm, or in thatother words we treat noise as signal, the noise is enhancedaccordingly by the α value. By observation, visual artifacts

(a) Original image (b) enhanced by α = 0.2

(c) enhanced by α = 0.5 (d) enhanced by α = 0.8

(e) Base layer (f) Base layer enhanced byα = 0.5

Fig. 3. Experimental results on “Lena”.

are not visible till Figure 3(d), when α = 0.8. A suppressionof high frequency details can be found around hat, whichis not expected. The reason is that the decomposition filteris failed. There exist some narrow high-light strips on hatboundary, and the decomposition filter put them into detaillayer as their scale is low. Due to the frequency analysisneeds the grayscale values of its neighboring pixels, the highcontrast signal suppresses the other details around it. However,the artifact is not strong when α ≤ 0.5. Moreover, it shouldbe noted that the over-flow and under-flow pixel grayscalevalues in the enhancement is simply truncated. Therefore theenhancement in high-light and dark regions is limited.

Two more examples are shown in Figures 4 and 5, respec-tively. Same as Figure 3, it can be observed that the α value hasa high correlation with enhancement level. The output imageslook realistic, and no obvious artifacts, such as halo, can beobserved.

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(a) Original image (b) enhanced by α = 0.2

(c) enhanced by α = 0.5 (d) enhanced by α = 0.8

Fig. 4. Experimental results on “Skull”.

IV. CONCLUSION

In this paper, a new image contrast enhancement algorithmis presented. The algorithm is based on a unified contrastperception model, which unifies human contrast perceptualresponse with four visual factors: physical contrast and fre-quency of a signal, background brightness and visual eccen-tricity. A edge-preserving decomposition filter is adapted in thealgorithm to separate input images into two layers: base layerand detail layer. Different enhancement schemes are appliedto the two layers. Perceptual lifting is applied to detail layerand stretching is applied to base layer. Both are based on thecontrast perception model. Experimental results confirmed theproposed image contrast enhancement algorithm can producenatural and clear image with least artifacts. Moreover, the en-hancement can be controlled by single perceptual enhancementparameter, which significantly simplifies the interactivity formany image processing applications.

REFERENCES

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[2] J.-L. Starck, F. Murtagh, E. J. Candes, and D. L. Donoho, “Gray andcolor image contrast enhancement by the curvelet transform,” IEEE Trans.Image Processing, vol. 12, no. 6, pp. 706–717, June 2003.

[3] C. Tomasi and R. Manduchi, “Bilateral filtering for gray and colorimages,” in Proceedings of ICCV’1998, Jan. 1998, pp. 839 – 846.

[4] S. Bae, S. Paris, and F. Durand, “Two-scale tone management forphotographic look,” ACM Trans. Graphics, vol. 25, no. 3, pp. 637 –645, July 2006.

[5] Z. Farbman, R. Fattal, D. Lischinski, and R. Szeliski, “Edge-preservingdecompositions for multi-scale tone and detail manipulation,” ACM Trans.Graphics, vol. 27, no. 3, pp. 67, July 2008.

(a) Original image (b) enhanced by α = 0.2

(c) enhanced by α = 0.5 (d) enhanced by α = 0.8

Fig. 5. Experimental results on “Genoa city”.

[6] Z. Lu and S. Rahardja, “Realistic hdr tone-mapping based on contrastperception matching,” in Proceedings of ICIP’2009, Cairo, Egypt, Nov.2009, pp. 1789–1792.

[7] Z. Lu and S. Rahardja, “A contrast perception matching based hdr tone-mapping operator,” in Proceedings of SIGGRAPH ASIA 2009: Poster,Yokohama, Japan, Dec. 2009, no. 51.

[8] S. Plainis and I. J. Murray, “Neurophysiological interpretation of humanvisual reaction times: effect of contrast, spatial frequency and luminance,”Neuropsychologia, vol. 38, no. 12, pp. 1555–1564, Oct. 2000.

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