Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts
This work solves the problem of super-resolution from raw bursts for photography applications, representing an incremental improvement over existing methods.
The paper tackles reconstructing high-resolution images from multiple low-resolution raw image bursts by addressing alignment, noise handling, and image priors, achieving state-of-the-art results on benchmarks with excellent qualitative outcomes on real smartphone and camera data.
This presentation addresses the problem of reconstructing a high-resolution image from multiple lower-resolution snapshots captured from slightly different viewpoints in space and time. Key challenges for solving this problem include (i) aligning the input pictures with sub-pixel accuracy, (ii) handling raw (noisy) images for maximal faithfulness to native camera data, and (iii) designing/learning an image prior (regularizer) well suited to the task. We address these three challenges with a hybrid algorithm building on the insight from Wronski et al. that aliasing is an ally in this setting, with parameters that can be learned end to end, while retaining the interpretability of classical approaches to inverse problems. The effectiveness of our approach is demonstrated on synthetic and real image bursts, setting a new state of the art on several benchmarks and delivering excellent qualitative results on real raw bursts captured by smartphones and prosumer cameras.