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Reconstruction-based super-resolution algorithms are widely employed for enhancing the quality of low-resolution face images. However, these algorithms are very sensitive to the registration errors of their input images. The registration errors aggravate when working with face images coming from video sequences. The longer the video the bigger is the registration error (due to the motion of the subject)...
Image super-resolution reconstruction (SRR) refers to a signal processing approach which produces a high-resolution (HR) image from observed multiple low-resolution (LR) images. In this paper, we propose a joint MAP formulation combining image registration, blur identification, and SRR together to deal with heavy aliasing in the observed LR images. A cyclic coordinate decent optimization procedure...
Image fusion was one of the most important technology of pattern recognition. This paper proposed a fast superresolution image reconstruction algorithm basing on image sequences. Iterative back-projection (IBP) technique was used to construct high resolution from image sequences. roughly registered basing on feature and then use Registration algorithm basing on Gray to optimize the result. Iterative...
We propose a super-resolution (SR) algorithm that takes into account inaccurate estimates of the registration parameters. When frames obey the assumed global motion model, these inaccurate estimates, along with the additive Gaussian noise in the low-resolution image sequence, result in different noise level for each frame. However, in case of existence of local motion and/or occlusion, regions that...
In many real applications traditional superresolution methods fail to provide high-resolution images due to objectionable blur and inaccurate registration of input low-resolution images. In this paper, we present a method of superresolution and blind deconvolution of video sequences and address problems of misregistration, local motion and change of illumination. The method processes the video by...
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