TransWaveNet: Transformer for Underwater Image Restoration with Wavelets

Underwater image restoration aims to improve the quality and visibility of images taken in underwater environments. These images find application in diverse fields like marine biology research, underwater archaeology, environmental monitoring, surveillance tasks, and offshore infrastructure inspection. However, the complexities of the underwater environment make these applications challenging, as light scattering and absorption cause blur, color cast, and reduced contrast in images. With the promising results on restoring underwater degraded images, existing approaches limit their performance in case of the above-mentioned complex and nonlinear degradation. In this research work, we propose a multi-directional wavelet coefficient space transformer model for underwater image deblurring and color restoration. Incorporating an attention mechanism within transformed spaces, our model dynamically adapts to underwater degradation. Additionally, we introduce a wavelet attention fusion transformer block for attention computation in the wavelet coefficient space, along with an edge-preserving wavelet down-sampling block to retain fine details and textures during downsampling. A thorough assessment of our method on real-world (UCCS, U45, SQUID) and synthetic (UIEB, UCDD) datasets, along with profound ablation studies, validates its edge over existing techniques. Further, we have evaluated our method for tasks such as depth estimation, and low-light enhancement and deblurring, demonstrating its versatility and broad applicability across various image processing tasks. The code is made available at: https://github.com/Priyanka01mishra/TransWaveNet.