Image contrast enhancement using an integration of recursive-overlapped contrast limited adaptive histogram specification and dual-image wavelet fusion for the high visibility of deep underwater image

Deep underwater images suffer from several problems, such as low contrast and visibility, which reduce the extraction rate of valuable information from the image. In this paper, we proposed an approach which integrates the three main steps of homomorphic filtering, recursive-overlapped CLAHS and dual-image wavelet fusion and thus increases the visibility of deep underwater images and the extraction rate of important data. Additionally, this approach implements homomorphic filtering to provide homogeneity in the illumination of the entire image which will be used for subsequent processes. Recursive-overlapped CLAHS refers to the recursive process of overlapped tiles of divided image channel, where half of a tile is processed twice with an adjacent tile. Half of the tile is overlapped with its next adjacent tile. Dual-image wavelet fusion merging two images obtained from the integration of upper- and lower-stretched histograms are applied with discrete wavelet transformation before the main process of wavelet fusion is implemented. Qualitative and quantitative results reveal that the proposed method outperforms the current state-of-the-art methods. The highest value of average quantitative evaluations in terms of entropy, average gradient, measure of enhancement (EME) and EME by entropy are 7.835, 12.802, 8.343 and 27.616, respectively, in the proposed method.