Restore image by learning from one image using efficient patch-based learning with Pereg
Restore image by learning from one image using efficient patch-based learning with Pereg
One-Shot Image Restoration
arXiv paper abstract https://arxiv.org/abs/2404.17426
arXiv PDF paper https://arxiv.org/pdf/2404.17426
Image restoration, or inverse problems in image processing, has long been an extensively studied topic.
... supervised learning ... popular ... to tackle this task. Unfortunately, ... demanding in ... computational resources and training data (sample complexity).
In addition, trained models are sensitive to domain changes, such as varying acquisition systems, signal sampling rates, resolution and contrast.
In this work, ... try to answer ... Can supervised learning ... learning from one image ... what is the minimal amount of patches required ... focus on an efficient patch-based learning framework that requires a single image input-output pair for training.
Experimental results demonstrate the applicability, robustness and computational efficiency of the proposed approach for supervised image deblurring and super-resolution.
... significant improvement of learning models' sample efficiency, generalization and time complexity ... for future real-time applications, and applied to other signals and modalities.
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