Blur2Blur: Blur Conversion for Unsupervised Image Deblurring on Unknown Domains

• URL: http://arxiv.org/abs/2403.16205v1
• Date: Sun, 24 Mar 2024 15:58:48 GMT
• Title: Blur2Blur: Blur Conversion for Unsupervised Image Deblurring on Unknown Domains
• Authors: Bang-Dang Pham, Phong Tran, Anh Tran, Cuong Pham, Rang Nguyen, Minh Hoai,
• Abstract summary: This paper presents an innovative framework designed to train an image deblurring algorithm tailored to a specific camera device. It works by transforming a blurry input image, which is challenging to deblur, into another blurry image that is more amenable to deblurring.
• Score: 19.573629029170128
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents an innovative framework designed to train an image deblurring algorithm tailored to a specific camera device. This algorithm works by transforming a blurry input image, which is challenging to deblur, into another blurry image that is more amenable to deblurring. The transformation process, from one blurry state to another, leverages unpaired data consisting of sharp and blurry images captured by the target camera device. Learning this blur-to-blur transformation is inherently simpler than direct blur-to-sharp conversion, as it primarily involves modifying blur patterns rather than the intricate task of reconstructing fine image details. The efficacy of the proposed approach has been demonstrated through comprehensive experiments on various benchmarks, where it significantly outperforms state-of-the-art methods both quantitatively and qualitatively. Our code and data are available at https://zero1778.github.io/blur2blur/

Related papers

• Real-World Efficient Blind Motion Deblurring via Blur Pixel Discretization [45.20189929583484]
  We decompose the deblurring (regression) task into blur pixel discretization and discrete-to-continuous conversion tasks. Specifically, we generate the discretized image residual errors by identifying the blur pixels and then transform them to a continuous form.
  arXiv  Detail & Related papers  (2024-04-18T13:22:56Z)
• Adaptive Window Pruning for Efficient Local Motion Deblurring [81.35217764881048]
  Local motion blur commonly occurs in real-world photography due to the mixing between moving objects and stationary backgrounds during exposure. Existing image deblurring methods predominantly focus on global deblurring. This paper aims to adaptively and efficiently restore high-resolution locally blurred images.
  arXiv  Detail & Related papers  (2023-06-25T15:24:00Z)
• MC-Blur: A Comprehensive Benchmark for Image Deblurring [127.6301230023318]
  In most real-world images, blur is caused by different factors, e.g., motion and defocus. We construct a new large-scale multi-cause image deblurring dataset (called MC-Blur) Based on the MC-Blur dataset, we conduct extensive benchmarking studies to compare SOTA methods in different scenarios.
  arXiv  Detail & Related papers  (2021-12-01T02:10:42Z)
• Human and Scene Motion Deblurring using Pseudo-blur Synthesizer [17.36135319921425]
  Present-day deep learning-based motion deblurring methods utilize the pair of synthetic blur and sharp data to regress any particular framework. We provide an on-the-fly blurry data augmenter that can be run during training and test stages. The proposed module is also equipped with hand-crafted prior extracted using the state-of-the-art human body statistical model.
  arXiv  Detail & Related papers  (2021-11-25T04:56:13Z)
• Clean Images are Hard to Reblur: A New Clue for Deblurring [56.28655168605079]
  We propose a novel low-level perceptual loss to make image sharper. To better focus on image blurriness, we train a reblurring module amplifying the unremoved motion blur. The supervised reblurring loss at training stage compares the amplified blur between the deblurred image and the reference sharp image. The self-blurring loss at inference stage inspects if the deblurred image still contains noticeable blur to be amplified.
  arXiv  Detail & Related papers  (2021-04-26T15:49:21Z)
• DeepI2P: Image-to-Point Cloud Registration via Deep Classification [71.3121124994105]
  DeepI2P is a novel approach for cross-modality registration between an image and a point cloud. Our method estimates the relative rigid transformation between the coordinate frames of the camera and Lidar. We circumvent the difficulty by converting the registration problem into a classification and inverse camera projection optimization problem.
  arXiv  Detail & Related papers  (2021-04-08T04:27:32Z)
• Deblurring by Realistic Blurring [110.54173799114785]
  We propose a new method which combines two GAN models, i.e., a learning-to-blurr GAN (BGAN) and learning-to-DeBlur GAN (DBGAN) The first model, BGAN, learns how to blur sharp images with unpaired sharp and blurry image sets, and then guides the second model, DBGAN, to learn how to correctly deblur such images. As an additional contribution, this paper also introduces a Real-World Blurred Image (RWBI) dataset including diverse blurry images.
  arXiv  Detail & Related papers  (2020-04-04T05:25:15Z)
• Beyond Camera Motion Blur Removing: How to Handle Outliers in Deblurring [14.244661742557899]
  When a scene has outliers such as saturated pixels, the captured blurred image becomes more difficult to restore. We first propose an edge-aware scale-recurrent network (EASRN) to conduct deblurring. Then a salient edge detection network is proposed to supervise the training process and constraint the edges restoration.
  arXiv  Detail & Related papers  (2020-02-24T12:32:04Z)
• Self-Supervised Linear Motion Deblurring [112.75317069916579]
  Deep convolutional neural networks are state-of-the-art for image deblurring. We present a differentiable reblur model for self-supervised motion deblurring. Our experiments demonstrate that self-supervised single image deblurring is really feasible.
  arXiv  Detail & Related papers  (2020-02-10T20:15:21Z)

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

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.