UMCTN: Real-World Underwater Image Enhancement Based on Transformer With Multikernel Convolution

The CNN-Transformer structure is widely applied to underwater image enhancement (UIE) tasks. However, previous studies have typically used structures similar to those in other image restoration scenarios, without specifically designing a unified structure that fully integrates the characteristics of convolution and Transformers for real-world underwater scenarios. Moreover, the inconsistency of color channel and spatial region attenuation of underwater images has not been given sufficient attention. To this end, this paper proposes a new UIE network, UMCTN, based on multi-kernel convolution Transformer. A multi-kernel convolution residual self-attention block (MCRA) was constructed. By designing a multi-kernel convolutional residual structure, it addresses the issue of critical feature information loss when convolution is applied to small-sized image patches in Transformers, which are widely adopted to reduce computational costs. It elegantly combines the characteristics of convolution and Transformers, endowing the network with a strong capability to capture both local and global dependencies. In addition, a feature fusion compensation module (FFCM) is proposed to supplement richer global perceptual features for MCRA, and It can effectively remove and restore spatial and color channels with more severe attenuation. Tests on the UIEB, UFO-120, and LSUI datasets show that UMCTN achieves better quantitative evaluation and visual performance compared to state-of-the-art (SOTA) schemes, with a maximum peak signal-to-noise ratio (PSNR) improvement of 1.93 dB. Detailed ablation studies validate the effectiveness of each component.