390 research outputs found
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
Blur2sharp: A gan-based model for document image deblurring
The advances in mobile technology and portable cameras have facilitated enormously the acquisition of text images. However, the blur caused by camera shake or out-of-focus problems may affect the quality of acquired images and their use as input for optical character recognition (OCR) or other types of document processing. This work proposes an end-to-end model for document deblurring using cycle-consistent adversarial networks. The main novelty of this work is to achieve blind document deblurring, i.e., deblurring without knowledge of the blur kernel. Our method, named “Blur2Sharp CycleGAN, ” generates a sharp image from a blurry one and shows how cycle-consistent generative adversarial networks (CycleGAN) can be used in document deblurring. Using only a blurred image as input, we try to generate the sharp image. Thus, no information about the blur kernel is required. In the evaluation part, we use peak signal to noise ratio (PSNR) and structural similarity index (SSIM) to compare the deblurring images. The experiments demonstrate a clear improvement in visual quality with respect to the state-of-the-art using a dataset of text images
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to derive an accurate blur kernel. Considering the critical role of image restoration in modern imaging systems to provide high-quality images under complex environments such as motion, undesirable lighting conditions, and imperfect system components, image deblurring has attracted growing attention in recent years. From the viewpoint of how to handle the ill-posedness which is a crucial issue in deblurring tasks, existing methods can be grouped into five categories: Bayesian inference framework, variational methods, sparse representation-based methods, homography-based modeling, and region-based methods. In spite of achieving a certain level of development, image deblurring, especially the blind case, is limited in its success by complex application conditions which make the blur kernel hard to obtain and be spatially variant. We provide a holistic understanding and deep insight into image deblurring in this review. An analysis of the empirical evidence for representative methods, practical issues, as well as a discussion of promising future directions are also presented
Let's Enhance: A Deep Learning Approach to Extreme Deblurring of Text Images
This work presents a novel deep-learning-based pipeline for the inverse
problem of image deblurring, leveraging augmentation and pre-training with
synthetic data. Our results build on our winning submission to the recent
Helsinki Deblur Challenge 2021, whose goal was to explore the limits of
state-of-the-art deblurring algorithms in a real-world data setting. The task
of the challenge was to deblur out-of-focus images of random text, thereby in a
downstream task, maximizing an optical-character-recognition-based score
function. A key step of our solution is the data-driven estimation of the
physical forward model describing the blur process. This enables a stream of
synthetic data, generating pairs of ground-truth and blurry images on-the-fly,
which is used for an extensive augmentation of the small amount of challenge
data provided. The actual deblurring pipeline consists of an approximate
inversion of the radial lens distortion (determined by the estimated forward
model) and a U-Net architecture, which is trained end-to-end. Our algorithm was
the only one passing the hardest challenge level, achieving over
character recognition accuracy. Our findings are well in line with the paradigm
of data-centric machine learning, and we demonstrate its effectiveness in the
context of inverse problems. Apart from a detailed presentation of our
methodology, we also analyze the importance of several design choices in a
series of ablation studies. The code of our challenge submission is available
under https://github.com/theophil-trippe/HDC_TUBerlin_version_1.Comment: This article has been published in a revised form in Inverse Problems
and Imagin
HyperCUT: Video Sequence from a Single Blurry Image using Unsupervised Ordering
We consider the challenging task of training models for image-to-video
deblurring, which aims to recover a sequence of sharp images corresponding to a
given blurry image input. A critical issue disturbing the training of an
image-to-video model is the ambiguity of the frame ordering since both the
forward and backward sequences are plausible solutions. This paper proposes an
effective self-supervised ordering scheme that allows training high-quality
image-to-video deblurring models. Unlike previous methods that rely on
order-invariant losses, we assign an explicit order for each video sequence,
thus avoiding the order-ambiguity issue. Specifically, we map each video
sequence to a vector in a latent high-dimensional space so that there exists a
hyperplane such that for every video sequence, the vectors extracted from it
and its reversed sequence are on different sides of the hyperplane. The side of
the vectors will be used to define the order of the corresponding sequence.
Last but not least, we propose a real-image dataset for the image-to-video
deblurring problem that covers a variety of popular domains, including face,
hand, and street. Extensive experimental results confirm the effectiveness of
our method. Code and data are available at
https://github.com/VinAIResearch/HyperCUT.gitComment: Accepted to CVPR 202
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