Adaptive Alignment Contrastive Learning of Degradation Prediction for Blind Image Super-Resolution

Blind super-resolution (BSR) is entering a new era focused on diverse and complex applications, where the tradeoff between generalization and performance prevents models from performing as they should. Model performance decreases when trained on multiple degraded images due to the inter-class and intra-class imbalances in degradation prediction, which consists of degradation sampling and estimation. The inter-class imbalance in degradation estimation causes inaccurate estimates, leading to severe artifacts in images. The intra-class imbalance in degradation sampling causes a long-tail problem, leading to model collapse and satisfactory results only in specific applications. To tackle these challenges, we propose adaptive alignment contrastive learning (AACL), which includes adaptive degradation sampling (ADS) and \(\sigma\) -alignment. ADS utilizes non-linear sampling by weighting the parameters of the degradation process for training uniformly degraded images, avoiding the long-tail problem. \(\sigma\) -alignment controls the SD among positive samples; we identify a subset with small degraded distance, which aids contrastive learning in extracting representations more effectively. We extend AACL to several CNN-based and Transformer-based methods by coming up with a 6 \(\times\) 6 fair architecture with degradation representation fusion block (DRFB) and degradation representation fusion group (DRFG). DRFB and DRFG are designed for degradation representation fusion and image reconstruction, respectively. We evaluate on six types of degradation, and the improvement experiments on synthesized images show that our method balances performance and generalization and is applicable to networks with different architectures. The comparison experiments show that our improved methods achieve promising results compared to SOTA methods. Code is available at: https://github.com/para999/AACL .