DPAMixerSR: An Efficient Degradation-Pattern-Aware Model for Image Super-Resolution

2026-09-26

AI Summary

This study addresses the limitation of existing super-resolution methods that overlook intrinsic degradation variations, resulting in suboptimal computational allocation and constrained reconstruction performance. To this end, we propose DPAMixerSR, a framework that couples a degradation-driven routing mechanism with structure-aligned sparse processing. Specifically, a degradation-aware ranking module evaluates regional degradation severity, routing heavily degraded regions to an adaptive sparse processing branch for multiscale propagation and bidirectional refinement, while directing mildly degraded regions to a lightweight convolutional branch. This selective strategy achieves superior structural recovery and perceptual fidelity across diverse super-resolution tasks while significantly reducing computational overhead, effectively balancing efficiency and reconstruction quality.

Abstract

While content-adaptive schemes have delivered notable advances in image super-resolution (SR), existing approaches typically focus on texture complexity and ignore intrinsic degradation factors (e.g., blur kernels or noise patterns), leading to suboptimal computation allocation and reconstruction performance. To remedy this, we propose DPAMixerSR, a degradation-pattern-aware framework that enables efficient SR through adaptive sparse computation. We design a lightweight Perceptual Degradation Ranking (PDR) module partitions the image into severely and mildly degraded patches, which are routed to the Adaptive Sparse Processing (ASP) and a lightweight convolutional branch, respectively. ASP performs structure-aligned, multi-scale sparse propagation and bidirectional refinement, while the convolutional branch enhances efficiency in mildly degraded regions. By coupling degradation-driven routing with structure-aligned sparse processing, DPAMixerSR establishes a self-regulating framework that dynamically balances computational efficiency and reconstruction fidelity. Extensive experiments on various SR tasks demonstrate that our DPAMixerSR achieves superior structural restoration and perceptual fidelity with markedly reduced computational overhead, providing a novel and scalable framework for degradation-aware, resource-efficient SR.

Problem

• Image Super-Resolution
• Degradation Pattern
• Computational Efficiency
• Reconstruction Performance

Innovation

• Degradation-Pattern-Aware
• Adaptive Sparse Computation
• Image Super-Resolution
• Perceptual Degradation Ranking
• Structure-Aligned Sparse Processing