Related papers: One-Shot Image Restoration

One-Shot Image Restoration

URL: http://arxiv.org/abs/2404.17426v2
Date: Mon, 23 Sep 2024 12:24:54 GMT
Title: One-Shot Image Restoration
Authors: Deborah Pereg,
Abstract summary: Experimental results demonstrate the applicability, robustness and computational efficiency of the proposed approach for supervised image deblurring and super-resolution. Our results showcase significant improvement of learning models' sample efficiency, generalization and time complexity.
Score: 0.0
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: Image restoration, or inverse problems in image processing, has long been an extensively studied topic. In recent years supervised learning approaches have become a popular strategy attempting to tackle this task. Unfortunately, most supervised learning-based methods are highly demanding in terms of 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, we try to answer a fundamental question: Can supervised learning models generalize well solely by learning from one image or even part of an image? If so, then what is the minimal amount of patches required to achieve acceptable generalization? To this end, we 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. Our results showcase significant improvement of learning models' sample efficiency, generalization and time complexity, that can hopefully be leveraged for future real-time applications, and applied to other signals and modalities.

Related papers

Oracle Bone Script Similiar Character Screening Approach Based on Simsiam Contrastive Learning and Supervised Learning [2.151884597519634]
This project proposes a new method that uses fuzzy comprehensive evaluation method to integrate ResNet-50 self-supervised and RepVGG supervised learning. The source image dataset HWOBC oracle is taken as input, the target image is selected, and the most similar image is output in turn without any manual intervention.
arXiv Detail & Related papers (2024-08-13T11:00:51Z)
Data Attribution for Text-to-Image Models by Unlearning Synthesized Images [71.23012718682634]
The goal of data attribution for text-to-image models is to identify the training images that most influence the generation of a new image. We propose a new approach that efficiently identifies highly-influential images.
arXiv Detail & Related papers (2024-06-13T17:59:44Z)
EfficientTrain++: Generalized Curriculum Learning for Efficient Visual Backbone Training [79.96741042766524]
We reformulate the training curriculum as a soft-selection function. We show that exposing the contents of natural images can be readily achieved by the intensity of data augmentation. The resulting method, EfficientTrain++, is simple, general, yet surprisingly effective.
arXiv Detail & Related papers (2024-05-14T17:00:43Z)
Efficient-3DiM: Learning a Generalizable Single-image Novel-view Synthesizer in One Day [63.96075838322437]
We propose a framework to learn a single-image novel-view synthesizer. Our framework is able to reduce the total training time from 10 days to less than 1 day.
arXiv Detail & Related papers (2023-10-04T17:57:07Z)
Unsupervised Deep Learning-based Pansharpening with Jointly-Enhanced Spectral and Spatial Fidelity [4.425982186154401]
We propose a new deep learning-based pansharpening model that fully exploits the potential of this approach. The proposed model features a novel loss function that jointly promotes the spectral and spatial quality of the pansharpened data. Experiments on a large variety of test images, performed in challenging scenarios, demonstrate that the proposed method compares favorably with the state of the art.
arXiv Detail & Related papers (2023-07-26T17:25:28Z)
EfficientTrain: Exploring Generalized Curriculum Learning for Training Visual Backbones [80.662250618795]
This paper presents a new curriculum learning approach for the efficient training of visual backbones (e.g., vision Transformers) As an off-the-shelf method, it reduces the wall-time training cost of a wide variety of popular models by >1.5x on ImageNet-1K/22K without sacrificing accuracy.
arXiv Detail & Related papers (2022-11-17T17:38:55Z)
Improving generalization with synthetic training data for deep learning based quality inspection [0.0]
supervised deep learning requires a large amount of annotated images for training. In practice, collecting and annotating such data is costly and laborious. We show the use of randomly generated synthetic training images can help tackle domain instability.
arXiv Detail & Related papers (2022-02-25T16:51:01Z)
Few-Cost Salient Object Detection with Adversarial-Paced Learning [95.0220555274653]
This paper proposes to learn the effective salient object detection model based on the manual annotation on a few training images only. We name this task as the few-cost salient object detection and propose an adversarial-paced learning (APL)-based framework to facilitate the few-cost learning scenario.
arXiv Detail & Related papers (2021-04-05T14:15:49Z)
Shared Prior Learning of Energy-Based Models for Image Reconstruction [69.72364451042922]
We propose a novel learning-based framework for image reconstruction particularly designed for training without ground truth data. In the absence of ground truth data, we change the loss functional to a patch-based Wasserstein functional. In shared prior learning, both aforementioned optimal control problems are optimized simultaneously with shared learned parameters of the regularizer.
arXiv Detail & Related papers (2020-11-12T17:56:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.