788 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
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Computational Cameras: Approaches, Benefits and Limits
A computational camera uses a combination of optics and software to produce images that cannot be taken with traditional cameras. In the last decade, computational imaging has emerged as a vibrant field of research. A wide variety of computational cameras have been demonstrated - some designed to achieve new imaging functionalities and others to reduce the complexity of traditional imaging. In this article, we describe how computational cameras have evolved and present a taxonomy for the technical approaches they use. We explore the benefits and limits of computational imaging, and describe how it is related to the adjacent and overlapping fields of digital imaging, computational photography and computational image sensors
Computational Flash Photography through Intrinsics
Flash is an essential tool as it often serves as the sole controllable light
source in everyday photography. However, the use of flash is a binary decision
at the time a photograph is captured with limited control over its
characteristics such as strength or color. In this work, we study the
computational control of the flash light in photographs taken with or without
flash. We present a physically motivated intrinsic formulation for flash
photograph formation and develop flash decomposition and generation methods for
flash and no-flash photographs, respectively. We demonstrate that our intrinsic
formulation outperforms alternatives in the literature and allows us to
computationally control flash in in-the-wild images.Comment: 9 pages, 15 figures. Accepted to CVPR 2023. Project page:
http://yaksoy.github.io/intrinsicFlash
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