Fundamentals of digital image processing
Fundamentals of digital image processing
Data compression: the complete reference
Data compression: the complete reference
Image and Video Compression for Multimedia Engineering
Image and Video Compression for Multimedia Engineering
Estimating Piecewise-Smooth Optical Flow with Global Matching and Graduated Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Techniques for Data Analysis, Second Edition
Statistical Techniques for Data Analysis, Second Edition
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Digital image stabilization with sub-image phase correlation based global motion estimation
IEEE Transactions on Consumer Electronics
IEEE Transactions on Image Processing
A new diamond search algorithm for fast block-matching motion estimation
IEEE Transactions on Image Processing
Extension of phase correlation to subpixel registration
IEEE Transactions on Image Processing
Subpixel estimation of shifts directly in the Fourier domain
IEEE Transactions on Image Processing
A motion-compensated spatio-temporal filter for image sequences with signal-dependent noise
IEEE Transactions on Circuits and Systems for Video Technology
The gray prediction search algorithm for block motion estimation
IEEE Transactions on Circuits and Systems for Video Technology
Polynomial search algorithms for motion estimation
IEEE Transactions on Circuits and Systems for Video Technology
Two-bit transform for binary block motion estimation
IEEE Transactions on Circuits and Systems for Video Technology
A fast hierarchical motion vector estimation algorithm using mean pyramid
IEEE Transactions on Circuits and Systems for Video Technology
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This correspondence presents a novel approach for translational motion estimation based on the phase of the Fourier transform. It exploits the equality between the averaging of a group of successive frames and the convolution of the reference one with an impulse train function. The use of suitable space filling curves allows to reduce the error in motion estimation making the proposed approach robust under noise. Experimental results show that the proposed approach outperforms available techniques in terms of objective (PSNR) and subjective quality with a lower computational effort.