12,420 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
CD-COCO: A Versatile Complex Distorted COCO Database for Scene-Context-Aware Computer Vision
The recent development of deep learning methods applied to vision has enabled
their increasing integration into real-world applications to perform complex
Computer Vision (CV) tasks. However, image acquisition conditions have a major
impact on the performance of high-level image processing. A possible solution
to overcome these limitations is to artificially augment the training databases
or to design deep learning models that are robust to signal distortions. We opt
here for the first solution by enriching the database with complex and
realistic distortions which were ignored until now in the existing databases.
To this end, we built a new versatile database derived from the well-known
MS-COCO database to which we applied local and global photo-realistic
distortions. These new local distortions are generated by considering the scene
context of the images that guarantees a high level of photo-realism.
Distortions are generated by exploiting the depth information of the objects in
the scene as well as their semantics. This guarantees a high level of
photo-realism and allows to explore real scenarios ignored in conventional
databases dedicated to various CV applications. Our versatile database offers
an efficient solution to improve the robustness of various CV tasks such as
Object Detection (OD), scene segmentation, and distortion-type classification
methods. The image database, scene classification index, and distortion
generation codes are publicly available
\footnote{\url{https://github.com/Aymanbegh/CD-COCO}
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