1,656 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
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
Learnable Blur Kernel for Single-Image Defocus Deblurring in the Wild
Recent research showed that the dual-pixel sensor has made great progress in
defocus map estimation and image defocus deblurring. However, extracting
real-time dual-pixel views is troublesome and complex in algorithm deployment.
Moreover, the deblurred image generated by the defocus deblurring network lacks
high-frequency details, which is unsatisfactory in human perception. To
overcome this issue, we propose a novel defocus deblurring method that uses the
guidance of the defocus map to implement image deblurring. The proposed method
consists of a learnable blur kernel to estimate the defocus map, which is an
unsupervised method, and a single-image defocus deblurring generative
adversarial network (DefocusGAN) for the first time. The proposed network can
learn the deblurring of different regions and recover realistic details. We
propose a defocus adversarial loss to guide this training process. Competitive
experimental results confirm that with a learnable blur kernel, the generated
defocus map can achieve results comparable to supervised methods. In the
single-image defocus deblurring task, the proposed method achieves
state-of-the-art results, especially significant improvements in perceptual
quality, where PSNR reaches 25.56 dB and LPIPS reaches 0.111.Comment: 9 pages, 7 figure
Learning Blind Motion Deblurring
As handheld video cameras are now commonplace and available in every
smartphone, images and videos can be recorded almost everywhere at anytime.
However, taking a quick shot frequently yields a blurry result due to unwanted
camera shake during recording or moving objects in the scene. Removing these
artifacts from the blurry recordings is a highly ill-posed problem as neither
the sharp image nor the motion blur kernel is known. Propagating information
between multiple consecutive blurry observations can help restore the desired
sharp image or video. Solutions for blind deconvolution based on neural
networks rely on a massive amount of ground-truth data which is hard to
acquire. In this work, we propose an efficient approach to produce a
significant amount of realistic training data and introduce a novel recurrent
network architecture to deblur frames taking temporal information into account,
which can efficiently handle arbitrary spatial and temporal input sizes. We
demonstrate the versatility of our approach in a comprehensive comparison on a
number of challening real-world examples.Comment: International Conference on Computer Vision (ICCV) (2017
Variant-Depth Neural Networks for Deblurring Traffic Images in Intelligent Transportation Systems
Intelligent transportation systems (ITS) with surveillance cameras capture traffic images or videos. However, images or videos in ITS often encounter blurs due to various reasons. Considering resource limitations, although recent technologies make progress in image-deblurring, there are still challenges in applying image-deblurring models in practical transportation systems: the model size and the running time. This work proposes an artful variant-depth network (VDN) to address the challenges. We design variant-depth sub-networks in a coarse-to-fine manner to improve the deblurring effect. We also adopt a new connection namely stack connection to connect all sub-networks to reduce the running time and model size while maintaining high deblurring quality. We evaluate the proposed VDN with the state-of-the-art (SOTA) methods on several typical datasets. Results on Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) show that the VDN outperforms SOTA image-deblurring methods. Furthermore, the VDN also has the shortest running time and the smallest model size
Distributed Deblurring of Large Images of Wide Field-Of-View
Image deblurring is an economic way to reduce certain degradations (blur and
noise) in acquired images. Thus, it has become essential tool in high
resolution imaging in many applications, e.g., astronomy, microscopy or
computational photography. In applications such as astronomy and satellite
imaging, the size of acquired images can be extremely large (up to gigapixels)
covering wide field-of-view suffering from shift-variant blur. Most of the
existing image deblurring techniques are designed and implemented to work
efficiently on centralized computing system having multiple processors and a
shared memory. Thus, the largest image that can be handle is limited by the
size of the physical memory available on the system. In this paper, we propose
a distributed nonblind image deblurring algorithm in which several connected
processing nodes (with reasonable computational resources) process
simultaneously different portions of a large image while maintaining certain
coherency among them to finally obtain a single crisp image. Unlike the
existing centralized techniques, image deblurring in distributed fashion raises
several issues. To tackle these issues, we consider certain approximations that
trade-offs between the quality of deblurred image and the computational
resources required to achieve it. The experimental results show that our
algorithm produces the similar quality of images as the existing centralized
techniques while allowing distribution, and thus being cost effective for
extremely large images.Comment: 16 pages, 10 figures, submitted to IEEE Trans. on Image Processin
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