scholarly journals Estimation of Subpixel Motion Using Bispectrum

2008 ◽  
Vol 2008 ◽  
pp. 1-5 ◽  
Author(s):  
El Mehdi Ismaili Aalaoui ◽  
Elhassane Ibn-Elhaj
Keyword(s):  
Motion Estimation ◽  
Frequency Domain ◽  
Video Processing ◽  
Cross Correlation ◽  
Image Sequences ◽  
Experimental Results ◽  
Phase Correlation ◽  
Estimation Methods ◽  
Noisy Image ◽  
Better Than

Motion estimation techniques are widely used in today's video processing systems. The frequently used techniques are frequency-domain motion estimation methods, most notably phase correlation (PC). If the image frames are corrupted by Gaussian noises, then cross-correlation and related techniques do not work well. In this paper, however, we have studied this topic from a viewpoint different from the above. Our scheme is based on the bispectrum method for sub-pixel motion estimation of noisy image sequences. Experimental results show that our proposed method performs significantly better than PC technique.

HOS-based method of global motion estimation for noisy image sequences

1999 ◽  
Vol 35 (16) ◽  
pp. 1320 ◽  
Author(s):  
E. Ibn-elhaj ◽  
D. Aboutajdine ◽  
S. Pateux ◽  
L. Morin
Keyword(s):  
Motion Estimation ◽  
Image Sequences ◽  
Noisy Image ◽  
Global Motion ◽  
Global Motion Estimation

Motion estimation and compensation from noisy image sequences: A new filtering scheme

2007 ◽  
Vol 25 (5) ◽  
pp. 686-694 ◽  
Author(s):  
Nawal Benmoussat ◽  
M. Faouzi Belbachir ◽  
Beloufa Benamar
Keyword(s):  
Motion Estimation ◽  
Image Sequences ◽  
Noisy Image

scholarly journals Advances in Motion Detection and Tracking of Human as a Target

2019 ◽  
Vol 8 (11) ◽  
pp. 3470-3473
Keyword(s):  
Motion Estimation ◽  
Video Compression ◽  
Video Processing ◽  
Image Data ◽  
Image Sequences ◽  
Stereo Correspondence ◽  
Image Domain ◽  
Motion Patterns ◽  
Estimation Algorithms ◽  
Detection And Tracking

Motion estimation of a target is the major area with higher computational complexity in video processing. It is the progression of discovery the motion patterns that describe the transformation from one frame to another in a sequence of video. Therefore, it is reasonable to carry out motion estimation only where movement is present. Image data in an image series remains mostly the same between frames along the target motion. To make use of the image statistics redundancy in image sequences, there is a need to guess motion. Motion estimation is valid for video compression improvement, stereo correspondence, object tracking and finding optical flow. Many precise methods have been proposed in the framework of one or more of these applications. Most motion estimation algorithms either operate directly in the image domain or finding the similar metric that measures how alike two pixels or two patches of pixels. In this paper, a review of a variety of motion estimation technique is presented

A Joint Method Based on Wavelet and Curvelet Transform for Image Denoising

2012 ◽  
Vol 532-533 ◽  
pp. 758-762
Author(s):  
Hua Wang ◽  
Jian Zhong Cao ◽  
Li Nao Tang ◽  
Zuo Feng Zhou
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
High Frequency ◽  
Curvelet Transform ◽  
Experimental Results ◽  
New Method ◽  
Good Effect ◽  
Noisy Image ◽  
Joint Method ◽  
Better Than

Wavelet transform is widely used and has good effect on image denoising. Wavelet transform has unique advantages in dealing with the smooth area of image but is not so perfect in high frequency areas such as the edges. However, curvelet transform can supply this gap when dealing with the high frequency areas because of the characteristic of anisotropy. In this paper, we proposed a new method which is based on the combination of wavelet transform and curvelet transform. Firstly, we detected the edges of the noisy-image using wavelet transform. Based on the edges we divided the image into two parts: the smoothness and the edges. Then, we used different transform methods to dispose different areas of the image, wavelet threshold denoising is used in smoothness while FDCT denoising is used in edges. Experimental results showed that we could get better visual effect and higher PSNR, which indicated that the proposed method is better than using wavelet transform or curvelet transform respectively.

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