用人类偏好优化图像超分辨率，提升视觉质量。
Perceptual Quality Optimization of Image Super-Resolution
- 设计双向注意力网络，实现图像级感知，避免繁琐的块采样。
- 基于自建数据集训练，预测结果与主观评价高度相关。
- 将感知评估融入训练，实现重建与视觉质量的闭环优化。
单图像超分辨率（SR）在深度学习推动下取得显著进展，但多数方法依赖失真导向损失或启发式感知先验，导致保真度与视觉质量之间的权衡。为此，本文提出一种高效感知双向注意力网络（Efficient-PBAN），明确优化生成结果以符合人类偏好。与基于块的质量评估模型不同，Efficient-PBAN避免大量块采样，实现高效的图像级感知。该框架在自建的超分辨率质量数据集上训练，涵盖多种前沿SR方法及其对应的人类评分。利用该数据集，Efficient-PBAN学习到与主观判断强相关的感知质量预测能力，并将其作为可微分的感知损失嵌入SR训练中，实现重建与感知评估的闭环对齐。大量实验表明，该方法在视觉质量上表现更优。代码已公开于 https://github.com/Lighting-YXLI/Efficient-PBAN。
原文摘要 · Abstract (English)
Single-image super-resolution (SR) has achieved remarkable progress with deep learning, yet most approaches rely on distortion-oriented losses or heuristic perceptual priors, which often lead to a trade-off between fidelity and visual quality. To address this issue, we propose an \textit{Efficient Perceptual Bi-directional Attention Network (Efficient-PBAN)} that explicitly optimizes SR towards human-preferred quality. Unlike patch-based quality models, Efficient-PBAN avoids extensive patch sampling and enables efficient image-level perception. The proposed framework is trained on our self-constructed SR quality dataset that covers a wide range of state-of-the-art SR methods with corresponding human opinion scores. Using this dataset, Efficient-PBAN learns to predict perceptual quality in a way that correlates strongly with subjective judgments. The learned metric is further integrated into SR training as a differentiable perceptual loss, enabling closed-loop alignment between reconstruction and perceptual assessment. Extensive experiments demonstrate that our approach delivers superior perceptual quality. Code is publicly available at https://github.com/Lighting-YXLI/Efficient-PBAN.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。