Visual-in-Visual: A Unified and Efficient Baseline for Image Restoration

Recent years have witnessed remarkable progress in image restoration, yet achieving both high performance and efficiency remains a persistent challenge. To address this issue, we present VIVNet, a strong and efficient unified baseline designed to balance accuracy and practicality. Drawing inspiration from the high efficiency of the human visual system, VIVNet embeds a biologically inspired micro visual module into each block of a macro U-shaped vision architecture. This module mimics key perceptual processes such as retinal encoding, lateral inhibition, and high-order processing by combining lightweight depth-wise convolutions for multi-receptive-field feature extraction, a similarity-aware weighting mechanism to emphasize informative signals, and high-order interactions implemented via iterative element-wise multiplication to capture complex dependencies. This design enhances the model's representational capacity while maintaining computational efficiency. Unlike most existing methods that are limited to narrow task settings, we evaluate VIVNet across a wide range of scenarios, including general, all-in-one, and composite degradation tasks, as well as ultra-high-definition (UHD), underwater, medical, and remote sensing datasets. Extensive experiments show that VIVNet delivers competitive performance with high efficiency.