Color correction and enhancement for underwater images is challenging due to attenuation and scattering. The underwater images often have low visibility and suffer from color bias. This paper presents a novel color correction method based on color filter array (CFA) and an enhancement method based on Retinex with dense pixels and adaptive linear histogram transformation for degraded color-biased underwater images. For any digital image in the RGB space, which is captured by digital camera with CFA, their RGB values are dependent and coupled because of the interpolation process. So we try to compensate red channel attenuation of underwater degraded images from the green channel and blue channel. Retinex model has been widely used to efficiently handle low brightness and blurred images. The McCann Retinex (MR) method selects a spiral path for pixel comparison to estimate illumination. However, the simple path selection doesn't include global light dark relationship of the whole image. So we design a scheme to gain much well-distributed and denser pixels to obtain more precise intensity of illumination. Besides, we design a piecewise linear function for histogram transform, which is adaptive to the whole RGB value. Experiments on a large number of underwater degraded images show that, the processed images by our method have clearer details and uniform visual effect for all channels in RGB color space and our method can also obtain good performance metrics. INDEX TERMS Underwater image enhancement, underwater image color correction, color filter array (CFA), Retinex, McCann Retinex, adaptive histogram transform.

Enhanced images by the traditional gamma correction (GC) method still have low contrast within high illuminance regions. In order to enhance the visibility in dark regions and simultaneously achieve high contrast in bright regions for low-light images, this paper proposes a novel method via a pair of complementary gamma functions (PCGF) by image fusion. We first define PCGF and then show its outstanding potential for low-light image enhancement by some preliminary experimental results. In order to release its performance and verify its effectiveness, we further design a simple enhancement method for low-light images based on it by an elaborately designed fusion strategy. Two input images for fusion are derived from the enhanced image by PCGF and that by proposed sharpening method, respectively. Experiments show that our proposed method can significantly enhance the detail and improve the contrast of low-light image. The qualitative experiment results show that the proposed method is effective and the comparative quantitative assessment shows that it outperforms other state-of-the-art methods. INDEX TERMS Gamma correction (GC); CRT gamma; pair of complementary gamma functions; lowlight image enhancement; image dehazing; underwater image restoration

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Sometimes it is very difficult to obtain high-quality images because of the limitations of image-capturing devices and the environment. Gamma correction (GC) is widely used for image enhancement. However, traditional GC perhaps cannot preserve image details and may even reduce local contrast within high-illuminance regions. Therefore, we first define two couples of quasi-symmetric correction functions (QCFs) to solve these problems. Moreover, we propose a novel low-light image enhancement method based on proposed QCFs by fusion, which combines a globally-enhanced image by QCFs and a locally-enhanced image by contrast-limited adaptive histogram equalization (CLAHE). A large number of experimental results showed that our method could significantly enhance the detail and improve the contrast of low-light images. Our method also has a better performance than other state-of-the-art methods in both subjective and objective assessments.

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Arithmetic Coding (AC) is a form of entropy encoding used in lossless data compression. It is a well-known, state of the art technique, in which the frequently seen symbols are encoded with fewer bits than rarely seen symbols. It has been widely used since last four decades. Many researchers worked on it to improve its performance and they successfully experimented with it. This technique has also been in use in combination with other techniques to gain surprising results. In this survey paper, an effort is made to recap a number of accomplishments from 1976 to 2017 regarding Arithmetic Coding. This study provides an insight for new researchers to know how this technique evolved with time and how major achievements were made using this technique. This paper gives a comparison of AC with another well-known technique named Huffman Coding. Comparison with its contemporary counterparts shows that it is better in performance almost in every situation.