Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution

• URL: http://arxiv.org/abs/2506.05607v1
• Date: Thu, 05 Jun 2025 21:40:21 GMT
• Title: Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution
• Authors: Shuchen Lin, Mingtao Feng, Weisheng Dong, Fangfang Wu, Jianqiao Luo, Yaonan Wang, Guangming Shi,
• Abstract summary: Real-world image super-resolution (Real-SR) is a challenging problem due to the complex degradation patterns in low-resolution images.<n>We propose an improved paradigm that frames Real-SR as a data-heterogeneous multi-task learning problem.
• Score: 51.79973519845773
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world image super-resolution (Real-SR) is a challenging problem due to the complex degradation patterns in low-resolution images. Unlike approaches that assume a broadly encompassing degradation space, we focus specifically on achieving an optimal balance in how SR networks handle different degradation patterns within a fixed degradation space. We propose an improved paradigm that frames Real-SR as a data-heterogeneous multi-task learning problem, our work addresses task imbalance in the paradigm through coordinated advancements in task definition, imbalance quantification, and adaptive data rebalancing. Specifically, we introduce a novel task definition framework that segments the degradation space by setting parameter-specific boundaries for degradation operators, effectively reducing the task quantity while maintaining task discrimination. We then develop a focal loss based multi-task weighting mechanism that precisely quantifies task imbalance dynamics during model training. Furthermore, to prevent sporadic outlier samples from dominating the gradient optimization of the shared multi-task SR model, we strategically convert the quantified task imbalance into controlled data rebalancing through deliberate regulation of task-specific training volumes. Extensive quantitative and qualitative experiments demonstrate that our method achieves consistent superiority across all degradation tasks.

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