NDLI: Recent advances in image deblurring

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Recent advances in image deblurring

Content Provider	ACM Digital Library
Author	Cho, Sunghyun Lee, Seungyong
Abstract	Motion blur is a common artifact that produces disappointing blurry images with inevitable information loss. Due to the nature of imaging sensors that accumulates incoming lights, a motion blurred image will be obtained if the camera sensor moves during exposure. Image (motion) de-blurring is a computational process to remove motion blurs from a blurred image to obtain a sharp latent image. Recently image de-blurring has become a popular topic in computer graphics and vision research, and excellent methods have been developed to improve the quality of de-blurred images and accelerate the computation speed. Image de-blurring has also a variety of applications in image enhancement software and camera industry, and a practical image de-blurring method with quality and speed would be a critical factor to improve the performance of image enhancement and camera systems. This course will first introduce the concepts, theoretical model, problem definition, and basic approach of image de-blurring. Blind deconvolution and non-blind deconvolution are two main topics of image de-blurring, which are classified by the existence of given kernel (or PSF; point spread function) information that describes the camera motion. For both blind deconvolution and non-blind deconvolution, challenges, classical methods, and recent research trends and successful methods will be presented. A PhotoShop demo will be given to show the performance of a recently developed fast motion de-blurring method. This course will also cover several advanced issues of image de-blurring, such as hardware based approaches, spatially-varying camera shakes, object motions, and video de-blurring. It will conclude with remaining challenges, such as outliers and noise, computation time, and quality assessment. There will be Q&A at the end of each presentation with a short discussion at the end of the course.
Starting Page	1
Ending Page	108
Page Count	108
File Format	PDF
ISBN	9781450326315
DOI	10.1145/2542266.2542272
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-11-19
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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