Multiresolution visual enhancement of hazy underwater scene

Abstract

Haze is an obvious phenomenon in the underwater scenario. The scene visibility reduces to a great extent due to haze, which makes the underwater visual surveillance quite a challenging task. In this article, we have exploited the multi-resolution ability of discrete wavelet transform and applied dark channel prior based transmission map estimation scheme to dehaze the highly degraded underwater image and restored the color. A three-fold scheme for dehazing of underwater sequences is proposed. In the first stage, image details are extracted using discrete wavelet transform followed by image negative operation. In the second stage, the negative of detail images are enhanced by the help of dark channel prior. The third stage is used for reconstruction, where the enhanced image details are used along with the single level approximate of the input image to get the dehazed underwater image using inverse discrete wavelet transform. The proposed scheme is tested with numerous standard underwater images, as well as the excavation images of Dwaraka (Dvārakā) underwater ruins. The effectiveness of the proposed scheme is justified by comparing it with different state-of-the-art image dehazing techniques. The quantitative evaluation has been carried out using five well established general purpose non-reference image quality indices namely BIQI (blind image quality index), BLIINDS (BLind Image Integrity Notator using DCT Statistics), DIIVINE (Distortion Identification-based Image Verity and INtegrity Evaluation), BRISQUE (Blind/Referenceless Image Spatial Quality Evaluator), and SSEQ (Spatial-Spectral Entropy-based Quality). Encouraging scores of 35.01, 30.105, 27.22, 30.10, and 27.8, are achieved for the BIQI, BRISQUE, SSEQ, DIIVINE, and BLIINDS, respectively. Four evaluation measures, exclusively designed for underwater scenarios (underwater image quality, contrast, sharpness, and colorfulness measures) are also used to test the performance of the proposed scheme. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.