RGB-Based Triple-Dual-Path Recurrent Network for Underwater Image Dehazing

In this paper, we present a powerful underwater image dehazing technique that exploits two image characteristics-RGB color channels and image features. In using RGB color channels, each color channel is decomposed into two units based on the similarities via the k-mean. This markedly improves the adaptability and identification of similar pixels, and thus reduces pixels with a weak correlation, leaving only pixels with a higher correlation. We use an infinite impulse response (IIR) in the triple-dual and parallel interaction structure to suppress hazed pixels via a pixel comparison and amplification to increase the visibility of even very minor features. This improves the visual perception of the final image, thus improving the overall usefulness and quality of the image. The softmax-weighted fusion is finally used to fuse the output color channel features to attain the final image. This preserves the color, leaving our proposed method's output very true to the original scene's. This is accomplished by taking advantage of adaptive learning based on the confidence levels of the pixel contribution variation in each color channel during subsequent fuses. The proposed technique both visually and objectively outperforms the existing methods in several rigorous tests.