An Effective Approach for the Underwater Color Image Enhancement Ching Soon Tan, Phooi Yee Lau, May Chi Lim, Jia Ann Hi Universiti Tunku Abdul Rahman Centre for Computing and Intelligent Systems (CCIS) Kampar, Malaysia {kelvintan91, jiaann, zizilmc}@1utar.my,
[email protected]
Phaik Eem Lim, Siew-Moi Phang
Tang Jung Low
Universiti Malaya Institute of Ocean & Earth Sciences (IOES) Kuala Lumpur, Malaysia {phaikeem,phang}@um.edu.my
Universiti Teknologi Petronas Department of Computer and Information Sciences (CIS) Tronoh, Malaysia
[email protected]
Abstract—The core problems of underwater imaging, especially for shallow waters, are the poor visibility and color diminish effect due to the physical lighting attenuation in aquatic world. This basically leads to the degradation of the clarity of the underwater images. In this paper, we propose an approach to improve the quality of underwater color images. Firstly, we adopted Gray World which is the most common white balancing technique to remove the dominant color cast which usually is either blue or green color then followed by applying contrast limited adaptive histogram equalization (CLAHE) to enhance the local contrast of the image and reduce the over-saturation effect caused by the Gray World. Lastly, an unsharp masking technique is used to highlight the visual detail in the image. This paper demonstrates our encouraging results using the experimental dataset obtained during the survey in Malaysian water. Keywords—Underwater image; Color correction; CLAHE; Gray World; Underwater lighting propagation
I. INTRODUCTION Underwater image undergoes color deterioration due to the scattering and absorption effect in the aquatic world. When the ambient light passes though water surface from air, it penetrates into water partly i.e. some light spectrum reflects back along its reverse path. This causes the lighting scattering effect [1][2]. On the other hand, the interaction of a lighting photon with water molecules brings about the lighting absorption [1][2]. When an observer goes down deeper photographing the aquatic world, the observed color appearance from the aquatic objects, e.g. marine animals, artifacts, seabed structure, are getting decreased until totally diminished due to the physical property in the aquatic environment. In general, the depth of the observer is proportional to the level of color degradation, where lights in red and the violet color spectrum could be strongly attenuated [1], while lights in blue-green color spectrum suffer from minimal attenuation. This is the primary reason why underwater image is being dominated by green-blue color. Moreover, the clarity of the underwater image is not limited by the underwater lighting propagation but also by the turbidity of the water or suspended particles in water. This typically This work is supported by the UTAR Research Fund Project No. IPSR/RMC/UTARRF/2013-C2/L03 “A New Framework for the identification of Biodiversity Abundances for Underwater Species in Malaysian Waters” from the Universiti Tunku Abdul Rahman, Malaysia.
characterize an unprocessed underwater image with low contrast, inherent featureless, blurring and lose of true color. Due to a growing underwater imaging application in the oceanographic works, it is necessary to acquire a clear underwater image before the scientific tasks begin. Recently, there exist many works which attempt to either enhance the underwater image or restore the color of the distorted underwater image. Chambah et al. [3] proposed an underwater image color correction method based on automated color equalization (ACE). Chiang and Chen [4] proposed an algorithm to restore underwater images that combines a dehazing algorithm with wavelength compensation (WCID). Iqbal et al. [5] applied contrast stretching on RGB then followed by saturation and intensity stretching on HSI color space to solve the poor visibility of the underwater images. The objective of the related works [3][4][5] is re-pursued in this paper on underwater videos obtained in Malaysian tropical water. That is, to reconstruct the color and enhance the quality of the underwater image making them closer to the human visual perception. The remaining of this paper is organized as follows. Section II presents the proposed approach. Section III provides the experiment results and a brief discussion. Lastly, section IV concludes the paper. II. THE PROPOSED ENHANCING APPROACH The proposed approach is depicted in a flow diagram shown in Fig. 1. After acquiring the underwater image, it is initially applied with Gray World method based on RGB color space to remove the color cast of the aquatic scene. It is followed by applying the contrast limited adaptive histogram equalization (CLAHE) on RGB color space to enhance the local contrast of the image. Unsharp masking is used as a postprocessing step to sharpen the corrected image.
Fig. 1. Process flow of the proposed approach .
fx, y 1∝ Ix. y /∝ Ix, y ∙ Gx, y
2.1. Gray World Gray World [6] is one of the white balancing techniques, which assumes that the average of the surface reflectance in the scene is achromatic. The basic concept of gray world algorithm is two folds. The first is to estimate the white point in the image. Secondly, the color cast is compensated based on the estimated illumination value. Although it has been widely used to remove the color cast over the image distorted by the colored illumination shift, the image taken in the aquatic environment also faces the similar problem since it typically covers by a layer of blue-green element. In our proposed approach, gray world is used as the first step to perform white balancing in the image. Gray world algorithm is implemented on RGB color space, as mathematically expressed in (1),
(2)
where f(x,y) is the final enhanced image by subtracting the Gaussian blurred image Ix, y ∙ Gx, y from the original image Ix. y . Note that Ix. y is the output image after step 2.2, ∝ is the positive scaling factor to control the level of contrast of countershading. Refer to [8], ∝ value is normally assigned to be 0.5. In order to make the edge feature over the image to be more apparent and sharpness, unsharp masking works on the narrow high pass filter, Gx, y , that is an Gaussian function with 3x3 kernel [8] .
1 ,
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1 ,
Fig. 2. Our self-designed equipment for the underwater video recording.
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where every pixel in a 8-bit color image have red, green and blue color channels that are respectively denoted as
(a) Clip limit = 0.01
(b) Clip Limit = 0.5
, , , and , . Dstx, y is the output image after performing gray world.
2.2. Contract Limited Adaptive Histogram Equalization (CLAHE) The previous stage emphasizes on removing the color cast of the aquatic scene out of the underwater images and increase the true color but the low visibility of the image does still exist. Moreover, over-saturation of the image may happen. Hence, in this step, contrast limited adaptive histogram equalization (CLAHE) [7] is applied to address these problems. CLAHE is the extended version of the adaptive histogram equalization to enhance the local contrast of the image. CLAHE algorithm partitions into small regions, called “tiles”, and each of which performs the histogram equalization individually. Later, it compiles the tiles by using bilinear interpolation to eliminate the unwanted artifacts boundary. CLAHE algorithm involves three parameters, clip limits, tile size and distribution type, where clip limits control the adjustment of the clipping points to prevent over-saturation of image. For the distribution type, we select the uniform distribution to distribute the gray level value in the histogram. In our implementation, we applied CLAHE in three color channels, Red, Green and Blue individually then merge them as an output image. 2.3. Unsharp masking As the post-processing step, a linear unsharp masking [8] is used to sharpen the images in order to highlight the visual details over the image. The operation of linear unsharp masking is mathematically expressed as (2),
Fig. 3. The comparision between the different output images (a), (b) that are respectively applied with clip limits 0.01 and 0.5 in our approach. The graph shows the relationship between the clip limit of CLAHE and PSNR.
III. EXPERIMENTAL RESULTS AND DISCUSSION The performance of the proposed approach was evaluated using our dataset, which are the underwater videos obtained by our self-designed equipment (Fig. 2) with an underwater color camera installed inside. This equipment was designed to float on water surface and expose down to record the underwater footage in a survey on shallow-water crustacean grounds off the Malaysia eastern coast island, Pulau Perhentian Kecil. In our approach that combines gray world and CLAHE, CLAHE algorithm involves clip limits that is sensitive to the overall
(a) Unprocessed image
(b) Histgram equalization
(c) Gray World
(d) Our proposed approach that combines Gray World and CLAHE
Fig. 4. Comparison of of output image that is uprocessed (a),applied histogram equalization (b), Gray World method (c), and our proposed approach (d). Noteably, the image in 3th row was obtained at the depth between 0 to 1 meter; The image in 4th row was obtained at the depth of approxinately 15 meters.
performance. However we have to determine a suitable value to it. Figure 3 shows the comparison between two output images which are respectively applied with different threshold value, 0.01 and 0.5 as clip limits. As seen, the color of the output image in Fig 3. (b) with the high clip limits is overequalized that leads to the degradation of the image naturalness. Moreover, we also used the PSNR (Peak Signal to Noise Ratio) to investigate the quality of the output images with different clip limits by compare an unprocessed image, 23, 4 , with the enhanced image, 53, 4 .
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1 MSE 23, 4 / 53 / 4 9
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