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Burst image processing is becoming increasingly popular in recent years.However, it is a challenging task since individual burst images undergomultiple degradations and often have mutual misalignments resulting in ghostingand zipper artifacts. Existing burst restoration methods usually do notconsider the mutual correlation and non-local contextual information amongburst frames, which tends to limit these approaches in challenging cases.Another key challenge lies in the robust up-sampling of burst frames. Theexisting up-sampling methods cannot effectively utilize the advantages ofsingle-stage and progressive up-sampling strategies with conventional and/orrecent up-samplers at the same time. To address these challenges, we propose anovel Gated Multi-Resolution Transfer Network (GMTNet) to reconstruct aspatially 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 featureaggregation, and Resolution Transfer Feature Up-sampler (RTFU) to up-scalemerged features and construct a high-quality output image. Detailedexperimental analysis on five datasets validates our approach and sets astate-of-the-art for burst super-resolution, burst denoising, and low-lightburst enhancement.

Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement