← 研踪 PaperTrailCVF · CVPR 2026:cvf:9662f6ee206eaa9d4cddfedef8389cf6 · 记录 #1
IAFMNet: Information-Aware Feature Modulation for Efficient Super-Resolution
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 03:53
摘要
Single Image Super-Resolution (SISR) aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) input, a task that becomes increasingly challenging under real-world computational constraints. However, most efficient SISR methods adopt lightweight, spatially uniform strategies that allocate equal computation and attention across all regions, ignoring the uneven distribution of visual complexity. From an information-theoretic perspective, textures and edges inherently carry more critical information, resulting in reconstruction errors that are disproportionately concentrated in these regions. This motivates the allocation of greater computational resources and attention to these informative areas. In this paper, we propose IAFMNet, an Information-Aware Feature Modulation network for efficient SR. At its core lies the Information Density Map (IDM), which is estimated in an unsupervised manner by minimizing the Information Entropy Loss, thereby highlighting informative regions with high estimated encoding costs. Guided by the IDM, IAFMNet adopts a synergistic dual-branch design: (1) a sparse convolution branch that dynamically allocates computation to informative areas while bypassing low-information regions, and (2) an implicit modulation branch that adaptively emphasizes complex regions through information-aware affine transformations. Extensive experiments demonstrate that IAFMNet effectively identifies informative regions and achieves superior visual fidelity with reduced computational overhead.
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记录 #1 · 2026 · 最新观测版本
IAFMNet: Information-Aware Feature Modulation for Efficient Super-Resolution
Single Image Super-Resolution (SISR) aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) input, a task that becomes increasingly challenging under real-world computational constraints. However, most efficient SISR methods adopt lightweight, spatially uniform strategies that allocate equal computation and attention across all regions, ignoring the uneven distribution of visual complexity. From an information-theoretic perspective, textures and edges inherently carry more critical information, resulting in reconstruction errors that are disproportionately concentrated in these regions. This motivates the allocation of greater computational resources and attention to these informative areas. In this paper, we propose IAFMNet, an Information-Aware Feature Modulation network for efficient SR. At its core lies the Information Density Map (IDM), which is estimated in an unsupervised manner by minimizing the Information Entropy Loss, thereby highlighting informative regions with high estimated encoding costs. Guided by the IDM, IAFMNet adopts a synergistic dual-branch design: (1) a sparse convolution branch that dynamically allocates computation to informative areas while bypassing low-information regions, and (2) an implicit modulation branch that adaptively emphasizes complex regions through information-aware affine transformations. Extensive experiments demonstrate that IAFMNet effectively identifies informative regions and achieves superior visual fidelity with reduced computational overhead.
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