Paper Info
Title
Camera noise model-based motion detection and blur removal for low-lighting images with moving objects
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.
Year
DOI
Venue
2014
10.1109/ICARCV.2014.7064431
ICARCV
Keywords
Field
DocType
bm3d denoising method,motion blur,mixed photon-electronic noise,for low-lighting images,statistical analysis,color images,signal-independent electronic noise,image denoising,image restoration,moving objects,blur removal,signal-dependent optical photon noise,camera noise model-based motion detection,camera variance-mean model,image motion analysis,noise reduction,robustness,noise measurement,noise,semiconductor device modeling
Value noise,Computer vision,Colors of noise,Noise measurement,Computer science,Dark-frame subtraction,Image noise,Artificial intelligence,Image restoration,Gaussian noise,Gradient noise
Conference
ISSN
Citations 
PageRank 
2474-2953
0
0.34
References 
Authors
9
3
Name
Order
Citations
PageRank
Shoulie Xie117720.80
Jinghong Zheng223316.81
Z. Li31578164.19