Fundamentals of digital image processing
Fundamentals of digital image processing
A Variational Framework for Retinex
International Journal of Computer Vision
Effective Gaussian Mixture Learning for Video Background Subtraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Switching bilateral filter with a texture/noise detector for universal noise removal
IEEE Transactions on Image Processing
Retinex by two bilateral filters
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
Properties and performance of a center/surround retinex
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
On the origin of the bilateral filter and ways to improve it
IEEE Transactions on Image Processing
High dynamic range image rendering with a retinex-based adaptive filter
IEEE Transactions on Image Processing
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
An Efficient Content-Based Image Enhancement in the Compressed Domain Using Retinex Theory
IEEE Transactions on Circuits and Systems for Video Technology
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Detecting the visually identical regions among successive frames for noisy videos, called visual identicalness detection (VID) in this paper, is a fundamental tool in video applications for lower power consumption and higher efficiency. In this paper, instead of performing VID on the original video signal or on the de-noised video signal, a Retinex based VID approach is proposed to perform VID on the Retinex signal to eliminate the noise influence introduced by imaging system. Several Retinex output generation approaches are compared, within which the proposed Cohen-Daubechies-Feauveau wavelet based approach is demonstrated to have better efficiency in detection and higher adaptability to the video content and noise severity. Compared with approaches performing detection in the de-noised images, the proposed algorithm presents up to 4.78 times higher detection rate for the videos with moving objects and up to 30.79 times higher detection rate for the videos with static scenes, respectively, at the same error rate. Also, an application of this technique is provided by integrating it into an H.264/AVC video encoder. Compared with compressing the de-noised videos using the existing fast algorithm, an average of 1.7dB performance improvement is achieved with up to 5.47 times higher encoding speed. Relative to the reference encoder, up to 32.47 times higher encoding speed is achieved without sacrificing the subjective quality.