VSRELL：低光照环境下视频超分辨率与增强的简单基线
VSRELL: A Simple Baseline for Video Super-Resolution and Enhancement in Low-Light Environment
把低光照增强与视频超分耦合进一个联合框架，用光照-噪声协同优化网络和光照敏感特征传播机制同时处理亮度校正与跨帧噪声累积，对做低层复原的人提供了一个可复用的『多退化解耦+时序特征门控』设计范式。
原文依据 We propose an integrated learning scheme of Video Super-Resolution and Enhancement in Low-Light environment, named VSRELL
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论本文提出一种低光照环境下视频超分辨率与增强的联合学习方案，命名为 VSRELL，旨在从低光照低分辨率（LLLR）序列中恢复光照良好的高分辨率（WIHR）序列。由于多种退化之间存在复杂的耦合关系，这一联合任务此前受到的关注相对较少。我们的方法对光照增强与时空超分辨率进行联合建模，以解耦相互交织的退化。具体而言，我们引入光照-噪声协同优化（INCO）网络，该网络采用动态窗口划分策略，在长时序序列的单帧内显式建模光照变化与噪声分布的物理先验。这有效抑制了跨帧噪声累积与光照闪烁，实现了运动补偿与亮度校正的同步优化。此外，我们引入光照敏感特征传播（ISFP）机制，利用分层光照感知门控单元自适应地调制特征通道响应。通过调整特征传播强度并采用记忆特征衰减策略，该机制能够增强高质量特征的权重、抑制误差累积传播并提升传输效率。实验表明，VSRELL 能够显式增强复原输出的亮度连续性与纹理保真度，并在整段视频中保持时序一致性。
英文原文摘要 1542 字符
We propose an integrated learning scheme of Video Super-Resolution and Enhancement in Low-Light environment, named VSRELL, which aims to recover Well-Illuminated High-Resolution (WIHR) sequence from Low-Light Low-Resolution (LLLR) counterparts. Due to the complex coupling of multiple degradations, this joint task has received relatively little attention. Our approach jointly models illumination enhancement and spatial-temporal super-resolution to disentangle intertwined degradations. Specifically, we introduce an Illumination-Noise Co-Optimization (INCO) network that employs a dynamic window partitioning strategy to explicitly model physical priors of illumination variations and noise distributions within individual frames of a long-term sequence. This effectively suppresses cross-frame noise accumulation and illumination flickering, achieving simultaneous optimization of motion compensation and brightness correction.Additionally, an Illumination-Sensitive Feature Propagation (ISFP) mechanism is introduced, which utilizes hierarchical illumination-sensing gating unit to adaptively modulate feature channel responses. By adjusting feature propagation intensity and using memory feature attenuation strategy, it can enhance the weighting of high-quality features and suppress error accumulation propagation and strengthen transmission efficiency. The experiments show that VSRELL can explicitly strengthen the brightness continuity and texture fidelity of the restored output, maintaining temporal consistency across the video.