Adaptive weighted multiscale retinex for underwater image enhancement

• Published in: Engineering applications of artificial intelligence Vol. 123; p. 106457
• Main Authors: Li, Dayi, Zhou, Jingchun, Wang, Shiyin, Zhang, Dehuan, Zhang, Weishi, Alwadai, Raghad, Alenezi, Fayadh, Tiwari, Prayag, Shi, Taian
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2023
• Subjects: Gradient domain fusion; Multiscale retinex; Underwater image; Visual perception; Gradient domain fusion; Underwater image; Multiscale retinex; Visual perception
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2023.106457

Vision-dependent underwater vehicles are widely used in seabed resource exploration. The visual perception system of underwater vehicles relies heavily on high-quality images for its regular operation. However, underwater images taken underwater often have color distortion, blurriness, and poor contrast. To address these degradation issues, we develop an adaptive weighted multiscale retinex (AWMR) method for enhancing underwater images. To utilize the local detail features, we first divide the image into multiple sub-blocks and calculate the detail sparsity index for each one. Then, we combine the global detail sparsity index with the local detail sparsity indices to determine the optimal scale parameter and corresponding weights for each sub-block. We apply retinex processing to each sub-block using these parameters and then subject the processed sub-blocks to detail enhancement, color correction, and saturation correction. Finally, we use a gradient domain fusion method based on structure tensors to fuse the corrected and enhanced sub-blocks and obtain the final output image. Our approach improves underwater images through comparisons with current state-of-the-art (SOTA) techniques on several open-source datasets, both quality, and performance. •A new correlation coefficient is designed to balance saturation and luminance.•A dynamic parameter is suggested to improve the robustness of color correction for various scene images.•A gradient domain fusion technique utilizing structure tensors is developed to reduce the blocking artifacts.