Self-MAR: Self-Supervised Multi-Stage Autoregressive Model for Water-Related Optical Image Enhancement

We propose Self-MAR, a self-supervised paradigm for water-related optical image enhancement that enables input-only restoration via the proposed VAR-inspired Multi-stage AutoRegressive model, in which each restoration stage is conditioned on the preceding restored state. The intricate interactions between light and the water medium introduce residual degradations that are difficult to model explicitly, thereby limiting the effectiveness of existing methods that rely heavily on synthetic data or sparse annotations. Furthermore, acquiring paired ground-truth data in real underwater environments remains challenging, giving rise to the triangular dilemma in underwater image enhancement (UIE). To address these issues, Self-MAR incorporates three key components: 1) We design a multi-stage, coarse-to-fine reconstruction architecture that progressively recovers image structure and suppresses content-coupled underwater object residuals (ORs) at different resolutions, transforming OR removal into structure-aware controllable image generation. 2) We construct an asymmetric phase-amplitude interaction that combines the structural phase of the current observation with the corrected spectral-energy distribution of the preceding restoration estimate. This interaction preserves spatial organization during stage-wise upsampling and propagates corrected color-energy statistics from coarse to fine. 3) We propose the Churning constraint mechanism to preserve the original perceptual features of the input image and prevent semantic drift during the self-supervised training process. Self-MAR derives supervision targets from the inputs themselves: underwater inputs generate OR-removal targets, while in-air inputs generate structural self-reconstruction targets from their own content.Extensive experiments demonstrate competitive full-reference restoration quality, leading performance across complementary no-reference metrics (UIQM, UCIQE, NIQE, and URANKER), efficient inference, and consistent performance across UIEB C60, UCCS, U45, and Z700. Downstream evaluation on SeaThru-D1 further shows the lowest combined depth-consistency error among thecompared state-of-the-art enhancement methods.