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A difficult challenge in obtaining clear images in underwater environment is because of poor visibility of objects due to light attenuation and color distortion. A solution to this is to do some form of enhancement of the image that eventually leads to better visualization. This paper presents a comparative analysis of three different enhancements techniques: contrast stretching (CS), histogram equalization (HE) and contrast limited adaptive histogram equalization (CLAHE) in the RGB and HSV color spaces for underwater images. Besides visual inspection, two different quantitative performance evaluation metrics, namely the Edge Contrast (EC) and BRISQUE, are used to assess the quality of the enhanced underwater images. Experimental results show that the CLAHE method performs better than CS and HE methods in both color spaces from the quality scores obtained in the EC as well as with the subjective evaluations.

This paper presents an image block classification method using Tchebichef moments (TMs) and support vector machine (SVM). The test images are divided into non-overlapping 16 × 16 blocks and transformed into moment domain using Discrete Tchebichef Transform. These moment features are then used in the image content (block) classification. SVM is used for learning and classifying the blocks into three types: "plain", "edge" and "texture", based on their moment energy level. Experimental results show that the proposed method works well and the classification accuracy is 98.7%.

Light scattering and absorption of light in water cause underwater images to be poorly contrasted, haze and dominated by a single color cast. A solution to this is to find methods to improve the quality of the image that eventually leads to better visualization. We propose an integrated approach using Adaptive Gray World (AGW) and Differential Gray-Levels Histogram Equalization for Color Images (DHECI) to remove the color cast as well as improve the contrast and colorfulness of the underwater image. The AGW is an adaptive version of the GW method where apart from computing the global mean, the local mean of each channel of an image is taken into consideration and both are weighted before combining them. It is applied to remove the color cast, thereafter the DHECI is used to improve the contrast and colorfulness of the underwater image. The results of the proposed method are compared with seven state-of-the-art methods using qualitative and quantitative measures. The experimental results showed that in most cases the proposed method produced better quantitative scores than the compared methods.

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