Nonlinear Camera Response Functions and Image Deblurring: Theoretical Analysis and Practice

This paper investigates the role that nonlinear camera response functions (CRFs) have on image deblurring. We present a comprehensive study to analyze the effects of CRFs on motion deblurring. In particular, we show how nonlinear CRFs can cause a spatially invariant blur to behave as a spatially var...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 35; no. 10; pp. 2498 - 2512
Main Authors: Yu-Wing Tai, Xiaogang Chen, Sunyeong Kim, Seon Joo Kim, Feng Li, Jie Yang, Jingyi Yu, Matsushita, Y., Brown, M. S.
Format: Journal Article
Language:English
Published: Los Alamitos, CA IEEE 01.10.2013
IEEE Computer Society
Subjects:
Algorithms
Applied sciences
Artificial Intelligence
Cameras
Computer science; control theory; systems
CRF estimation
Deconvolution
Detection, estimation, filtering, equalization, prediction
Estimation
Exact sciences and technology
Image edge detection
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image restoration
Information, signal and communications theory
Kernel
motion deblurring
Nonlinear camera response functions (CRFs)
Nonlinear Dynamics
Pattern Recognition, Automated - methods
Pattern recognition. Digital image processing. Computational geometry
Reproducibility of Results
Sensitivity and Specificity
Shape
Signal and communications theory
Signal, noise
Telecommunications and information theory
Computer vision
Image processing
Noise reduction
Blind deconvolution
Image restoration
Blurred image
Point spread function
Noisy image
Response function
Image analysis
Experimental result
Spatial distribution
Pulse response
Nonlinear camera response functions (CRFs)
Robustness
motion deblurring
Camera
CRF estimation
Signal to noise ratio
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:Get full text
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Summary:This paper investigates the role that nonlinear camera response functions (CRFs) have on image deblurring. We present a comprehensive study to analyze the effects of CRFs on motion deblurring. In particular, we show how nonlinear CRFs can cause a spatially invariant blur to behave as a spatially varying blur. We prove that such nonlinearity can cause large errors around edges when directly applying deconvolution to a motion blurred image without CRF correction. These errors are inevitable even with a known point spread function (PSF) and with state-of-the-art regularization-based deconvolution algorithms. In addition, we show how CRFs can adversely affect PSF estimation algorithms in the case of blind deconvolution. To help counter these effects, we introduce two methods to estimate the CRF directly from one or more blurred images when the PSF is known or unknown. Our experimental results on synthetic and real images validate our analysis and demonstrate the robustness and accuracy of our approaches.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2013.40