Burst image processing is becoming increasingly popular in recent years. However, it is a challenging task since individual burst images undergo multiple degradations and often have mutual misalignments resulting in ghosting and zipper artifacts. Existing burst restoration methods usually do not consider the mutual correlation and non-local contextual information among burst frames, which tends to limit these approaches in challenging cases. Another key challenge lies in the robust up-sampling of burst frames. The existing up-sampling methods cannot effectively utilize the advantages of single-stage and progressive up-sampling strategies with conventional and/or recent up-samplers at the same time. To address these challenges, we propose a novel Gated Multi-Resolution Transfer Network (GMTNet) to reconstruct a spatially precise high-quality image from a burst of low-quality raw images. GMTNet consists of three modules optimized for burst processing tasks: Multi-scale Burst Feature Alignment (MBFA) for feature denoising and alignment, Transposed-Attention Feature Merging (TAFM) for multi-frame feature aggregation, and Resolution Transfer Feature Up-sampler (RTFU) to up-scale merged features and construct a high-quality output image. Detailed experimental analysis on five datasets validates our approach and sets a state-of-the-art for burst super-resolution, burst denoising, and low-light burst enhancement.

翻译：突发图像处理近年在学术界日益受到关注。然而，由于单次突发图像序列中的各帧图像通常遭受多重退化，并常因相互错位导致重影与锯齿伪影，该任务极具挑战性。现有突发图像恢复方法通常未充分考虑突发帧间的相互关联性与非局部上下文信息，这使得这些方法在复杂场景中表现受限。另一关键挑战在于突发帧的稳健上采样：现有上采样方法无法同时有效结合单阶段与渐进式上采样策略的优势，亦无法同时利用传统及现代上采样器。针对上述问题，本文提出一种新颖的门控多分辨率传递网络（GMTNet），用于从低质量原始图像突发序列中重建空间精确的高质量图像。GMTNet包含三个针对突发处理任务优化的模块：用于特征去噪与对齐的多尺度突发特征对齐模块（MBFA）、用于多帧特征聚合的转置注意力特征融合模块（TAFM），以及用于放大融合特征并构建高质量输出图像的分辨率传递特征上采样器（RTFU）。在五个数据集上的详细实验分析验证了本方法在突发超分辨率、突发去噪及低光照突发增强任务中均达到了最先进性能。