ISSR-UNet: Intrinsic Supervision Shuffle Residual UNet for Underwater Image Restoration

Underwater image restoration is a challenging task due to color distortion and hazing effects caused by light absorption and scattering. We propose a novel image restoration architecture, the Intrinsic Supervision Shuffle Residual UNet (ISSR-UNet), based on the UNet framework. ISSR-UNet leverages Adaptive Selective Intrinsic Supervised Features and a cross-scale feature shuffling mechanism to enhance image restoration performance and preserve more image details. It incorporates four pivotal components: the Selective Residual (SR) block, the Cross-scale Feature Shuffling (CFS) module, the Multi-Degradation Supervision (MDS) module, and the Adaptive Selective Intrinsic Supervised Feature Extraction (ASISFE) module. Extensive experiments validate that ISSR-UNet achieves superior performance, surpassing state-of-the-art methods on underwater image restoration benchmarks.