An Efficient Approach for Underwater Image Improvement: Deblurring, Dehazing, and Color Correction

Abstract

As remotely operated underwater vehicles (ROV) and static underwater video and image collection platforms become more prevalent, there is a significant need for effective ways to increase the quality of underwater images at faster than real-time speeds. To this end, we present a novel state-of-the-art end-to-end deep learning architecture for underwater image enhancement focused on solving key image degradations related to blur, haze, and color casts and inference efficiency. Our proposed architecture builds from a minimal encoder-decoder structure to address these main underwater image degradations while maintaining efficiency. We use the discrete wavelet transform skip connections and channel attention modules to address haze and color corrections while preserving model efficiency. Our minimal architecture operates at 40 frames per second while scoring a structural similarity index (SSIM) of 0.8703 on the underwater image enhancement benchmark (UIEDB) dataset. These results show our method to be twice as fast as the previous state-of-the-art. We also present a variation of our proposed method with a second parallel deblurring branch for even more significant image improvement, which achieves an improved SSIM of 0.8802 while operating more efficiently than almost all comparable methods. The source code is available at https://github.com/alejorico98/underwater_ddc