Underwater image enhancement based on residual network and image formation model

Abstract

Our paper presents a new deep learning-based algorithm for improving the visual quality of images captured by underwater robots. The algorithm is designed to address the common issues of color distortion, low contrast, and lack of detail that often plague underwater images. By leveraging an image formation model, the proposed method is able to eliminate the effects of underwater environmental factors and enhance the color, detail, and overall visual appeal of the images. The performance of the proposed method is evaluated using objective metrics such as PSNR and SSIM, and the results demonstrate its effectiveness in improving the visual quality of underwater images. In addition, the proposed method is found to be computationally efficient, making it well-suited for use in real-time application. The proposed method has the potential to significantly improve the visual quality of underwater images and open up new opportunities for underwater exploration and conservation.