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Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts

Bruno Lecouat 1, 2 Jean Ponce 1 Julien Mairal 1 
1 Thoth - Apprentissage de modèles à partir de données massives
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann
2 WILLOW - Models of visual object recognition and scene understanding
DI-ENS - Département d'informatique - ENS Paris, Inria de Paris
Abstract : 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 super-resolution 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.
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Submitted on : Monday, August 23, 2021 - 12:12:37 PM
Last modification on : Wednesday, June 8, 2022 - 12:50:06 PM
Long-term archiving on: : Wednesday, November 24, 2021 - 6:13:35 PM


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  • HAL Id : hal-03323885, version 1



Bruno Lecouat, Jean Ponce, Julien Mairal. Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts. ICCV 2021 - International Conference on Computer Vision, Oct 2021, Virtual, France. pp.1-16. ⟨hal-03323885⟩



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