370 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
Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement
We address the problem of restoring a high-resolution face image from a
blurry low-resolution input. This problem is difficult as super-resolution and
deblurring need to be tackled simultaneously. Moreover, existing algorithms
cannot handle face images well as low-resolution face images do not have much
texture which is especially critical for deblurring. In this paper, we propose
an effective algorithm by utilizing the domain-specific knowledge of human
faces to recover high-quality faces. We first propose a facial component guided
deep Convolutional Neural Network (CNN) to restore a coarse face image, which
is denoted as the base image where the facial component is automatically
generated from the input face image. However, the CNN based method cannot
handle image details well. We further develop a novel exemplar-based detail
enhancement algorithm via facial component matching. Extensive experiments show
that the proposed method outperforms the state-of-the-art algorithms both
quantitatively and qualitatively.Comment: In IJCV 201
Learning Depth from Focus in the Wild
For better photography, most recent commercial cameras including smartphones
have either adopted large-aperture lens to collect more light or used a burst
mode to take multiple images within short times. These interesting features
lead us to examine depth from focus/defocus.
In this work, we present a convolutional neural network-based depth
estimation from single focal stacks. Our method differs from relevant
state-of-the-art works with three unique features. First, our method allows
depth maps to be inferred in an end-to-end manner even with image alignment.
Second, we propose a sharp region detection module to reduce blur ambiguities
in subtle focus changes and weakly texture-less regions. Third, we design an
effective downsampling module to ease flows of focal information in feature
extractions. In addition, for the generalization of the proposed network, we
develop a simulator to realistically reproduce the features of commercial
cameras, such as changes in field of view, focal length and principal points.
By effectively incorporating these three unique features, our network
achieves the top rank in the DDFF 12-Scene benchmark on most metrics. We also
demonstrate the effectiveness of the proposed method on various quantitative
evaluations and real-world images taken from various off-the-shelf cameras
compared with state-of-the-art methods. Our source code is publicly available
at https://github.com/wcy199705/DfFintheWild
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