31,122 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
UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition
Advances in image restoration and enhancement techniques have led to
discussion about how such algorithmscan be applied as a pre-processing step to
improve automatic visual recognition. In principle, techniques like deblurring
and super-resolution should yield improvements by de-emphasizing noise and
increasing signal in an input image. But the historically divergent goals of
the computational photography and visual recognition communities have created a
significant need for more work in this direction. To facilitate new research,
we introduce a new benchmark dataset called UG^2, which contains three
difficult real-world scenarios: uncontrolled videos taken by UAVs and manned
gliders, as well as controlled videos taken on the ground. Over 160,000
annotated frames forhundreds of ImageNet classes are available, which are used
for baseline experiments that assess the impact of known and unknown image
artifacts and other conditions on common deep learning-based object
classification approaches. Further, current image restoration and enhancement
techniques are evaluated by determining whether or not theyimprove baseline
classification performance. Results showthat there is plenty of room for
algorithmic innovation, making this dataset a useful tool going forward.Comment: Supplemental material: https://goo.gl/vVM1xe, Dataset:
https://goo.gl/AjA6En, CVPR 2018 Prize Challenge: ug2challenge.or
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