A super-resolution reconstruction algorithm is proposed. The algorithm is based on the idea of the sparse representation of signals, by using the fact that the sparsest representation of a sig- nal is unique as the co...A super-resolution reconstruction algorithm is proposed. The algorithm is based on the idea of the sparse representation of signals, by using the fact that the sparsest representation of a sig- nal is unique as the constraint of the patched-based reconstruction, and compensating residual errors of the reconstruction results both locally and globally to solve the distortion problem in patch-based reconstruction algorithms. Three reconstruction algorithms are compared. The results show that the images reconstructed with the new algorithm have the best quality.展开更多
In the process of image understanding,the human visual system(HVS)performs multiscale analysis on various objects.HVS primarily focuses on marginally conspicuous image patches located within or around distinct objects...In the process of image understanding,the human visual system(HVS)performs multiscale analysis on various objects.HVS primarily focuses on marginally conspicuous image patches located within or around distinct objects rather than scanning the image pixels point by point.Inspired by the HVS mechanism,in this paper,we aimed to describe and exploit multiscale decomposition-based patch detection models for automatic visual feature representation and object localization in images.Our investigation into mimicking and modeling the HVS to capture conspicuous sparse patches and their spatial distribution clues makes a profound contribution to the automatic comprehension and characterization of images by machines.This study demonstrates that the sparse patch-based visual representation with spatial center cues is intrinsically tolerant to object positioning and understanding beyond object variations in spatial position,multiresolution,and chrominance,which has significant implications for many vision-based automatic object grabbing and perception applications,such as robotics,human‒machine interaction,and unmanned aerial vehicles(UAVs).展开更多
基金Supported by the Basic Research Foundation of Beijing Institute of Technology(3050012211105)
文摘A super-resolution reconstruction algorithm is proposed. The algorithm is based on the idea of the sparse representation of signals, by using the fact that the sparsest representation of a sig- nal is unique as the constraint of the patched-based reconstruction, and compensating residual errors of the reconstruction results both locally and globally to solve the distortion problem in patch-based reconstruction algorithms. Three reconstruction algorithms are compared. The results show that the images reconstructed with the new algorithm have the best quality.
基金supported by National Natural Science Foundation of China(61976123,61601427)Taishan Young Scholars Program of Shandong Province+1 种基金Key Development Program for Basic Research of Shandong Province(ZR2020ZD44)Royal Society-K.C.Wong International Fellowship(NIF\R1\180909).
文摘In the process of image understanding,the human visual system(HVS)performs multiscale analysis on various objects.HVS primarily focuses on marginally conspicuous image patches located within or around distinct objects rather than scanning the image pixels point by point.Inspired by the HVS mechanism,in this paper,we aimed to describe and exploit multiscale decomposition-based patch detection models for automatic visual feature representation and object localization in images.Our investigation into mimicking and modeling the HVS to capture conspicuous sparse patches and their spatial distribution clues makes a profound contribution to the automatic comprehension and characterization of images by machines.This study demonstrates that the sparse patch-based visual representation with spatial center cues is intrinsically tolerant to object positioning and understanding beyond object variations in spatial position,multiresolution,and chrominance,which has significant implications for many vision-based automatic object grabbing and perception applications,such as robotics,human‒machine interaction,and unmanned aerial vehicles(UAVs).
