Camera noise model-based motion detection and blur removal for low-lighting images with moving objects

Camera noise model-based motion detection and blur removal for low-lighting images with moving objects
Title:
Camera noise model-based motion detection and blur removal for low-lighting images with moving objects
Other Titles:
2014 13th International Conference on Control Automation Robotics & Vision (ICARCV)
Keywords:
Publication Date:
10 December 2014
Citation:
S. Xie, J. Zheng and Z. Li, "Camera noise model-based motion detection and blur removal for low-lighting images with moving objects," Control Automation Robotics & Vision (ICARCV), 2014 13th International Conference on, Singapore, 2014, pp. 940-943. doi: 10.1109/ICARCV.2014.7064431
Abstract:
It is well known that modern CCD/CMOS digital cameras produce color images contaminated by mixed photon-electronic noise, which is a mixture of signal-dependent optical photon noise and signal-independent electronic noise. In statistical, variance of the mixed noise is a line function of mean intensity on the pixel. Based on this camera variance-mean model, we propose a fast and robust approach to generate a high quality image from a pair of noisy/blurred low-lighting images with moving objects along any directions. More precisely, camera noise variance model is employed to separate the effects of noise from moving objects on the images, followed by BM3D denoising method to reduce the noise of identified moving objects in the noisy image. Then motion blur in the blurred image is removed by a patching method, which is robust to object movements along any directions. We validate the effectiveness of our proposed approach on real images with moving objects in this paper.
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
ISBN:
978-1-4799-5199-4
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