DeMemSR: Transferable Degradation Representation Learning and Stable–Dynamic LR Memory Fusion for Diffusion-Based Real-World Image Super-Resolution

Diffusion-based real-world image super-resolution can benefit from both degradation-aware conditioning and adaptive interaction between low-resolution (LR) observations and evolving noisy latents. However, contrastive degradation learning may distinguish different degradation configurations using only a subset of degradation cues, without adequately retaining the degradation information relevant to restoration. Moreover, although bidirectional LR–latent interaction allows LR guidance to adapt to the evolving denoising state, repeatedly updating the LR stream may cause it to drift from the original observation, weakening its role as a faithful structural anchor. To address these issues, we present DeMemSR, a degradation-aware diffusion Transformer framework that combines Transferable Degradation Representation Learning (TDRL) with Stable–Dynamic LR Memory Fusion (SD-LRMF). TDRL combines contrastive degradation learning with cross-content re-degradation. While contrastive learning encourages the projected features to distinguish degradation configurations, re-degradation encourages the encoder to retain degradation information beyond what is needed for discrimination. A degradation-aware conditioner then uses the projected features to provide layer-specific guidance to the diffusion Transformer. SD-LRMF explicitly separates LR guidance into an immutable Stable LR Memory derived from the initial observation and an interaction-updated Dynamic LR State. Learnable layer-wise fusion re-anchors the dynamic state to the stable memory across network depth, preserving observation fidelity while retaining the flexibility of bidirectional interaction. Experiments on four real-world benchmarks show that DeMemSR achieves leading no-reference perceptual quality, including the highest MUSIQ and LIQE scores among the evaluated methods on all four datasets.