A novel hybrid customized color correction and Recurrent Convolutional Neural Networks approach for underwater image enhancement.

Underwater imaging suffers from various challenges such as color distortion, low contrast, and reduced visibility caused by light attenuation. Addressing these issues is crucial for improving the performance of computer vision tasks in underwater environments. Different approaches are proposed in the literature to enhance the underwater images, however the dynamically varying environmental conditions remains challenging. It requires adaptive approach to adjust the color dynamically and to estimate the features. In this study, we propose a novel hybrid approach for underwater image enhancement that integrates a customized color correction algorithm with Recurrent Convolutional Neural Networks (R-CNN), offering superior image enhancement capabilities compared to conventional methods. The proposed method comprises three key stages: color correction, recurrent feature extraction, and enhancement. The color correction algorithm goes beyond traditional method by adjusting the color channels based on dynamically estimated attenuation coefficients specific to underwater environments, effectively restoring natural color tones and also enhancing overall visual accuracy. The recurrent design enables the network to enhance feature representations by continuously refining them across iterations, thus preserving intricate scene details better than static CNNs. Finally, the novel enhancement strategy guided by the recurrently extracted features, further improves the clarity, contrast and visibility. Experimental evaluation conducted on diverse underwater image datasets demonstrate the superiority of proposed approach, offering significant improvements in image quality over state-of-the-art techniques. Qualitative and quantitative analyses reveal that the proposed method not only enhances images aesthetics but also facilitates better performance in downstream computer vision tasks such as object detection, recognition and environmental monitoring.