The performances of repaired image depend on the local information in the repaired area and the consistency between the repair directions with structural content.Image repair algorithm with texture information perform...The performances of repaired image depend on the local information in the repaired area and the consistency between the repair directions with structural content.Image repair algorithm with texture information performs well in repairing seriously damaged images,but it has bad performances when the images have the abundant structure information.The dual optimization image repair algorithm based on the linear structure and the optimal texture is proposed.The algorithm uses the double-constraint sparse model to reconstruct the missed information in large area in order to improve the clarity of repaired images.After adopting the preference of Criminisi priority,the image repair algorithm of self-similarity characteristics is proposed to improve the fault and fuzzy distortion phenomena in the repaired image.The results show that the proposed algorithm has more clarity in the image texture and structure and better effectiveness,and the peak signal-to-noise ratio of the repaired images by proposed algorithm is superior to that by other algorithms.展开更多
Image enhancement is a popular technique,which is widely used to improve the visual quality of images.While image enhancement has been extensively investigated,the relevant quality assessment of enhanced images remain...Image enhancement is a popular technique,which is widely used to improve the visual quality of images.While image enhancement has been extensively investigated,the relevant quality assessment of enhanced images remains an open problem,which may hinder further development of enhancement techniques.In this paper,a no-reference quality metric for digitally enhanced images is proposed.Three kinds of features are extracted for characterizing the quality of enhanced images,including non-structural information,sharpness and naturalness.Specifically,a total of 42 perceptual features are extracted and used to train a support vector regression(SVR) model.Finally,the trained SVR model is used for predicting the quality of enhanced images.The performance of the proposed method is evaluated on several enhancement-related databases,including a new enhanced image database built by the authors.The experimental results demonstrate the efficiency and advantage of the proposed metric.展开更多
We present dynamic mode decomposition (DMD) for studying the hairpin vortices generated by hemisphere protuberance measured by two-dimensional (2D) time-resolved (TR) particle image velocimetry (PIV) in a water channe...We present dynamic mode decomposition (DMD) for studying the hairpin vortices generated by hemisphere protuberance measured by two-dimensional (2D) time-resolved (TR) particle image velocimetry (PIV) in a water channel. The hairpins dynamic information is extracted by identifying their dominant frequencies and associated spatial structures. For this quasi-periodic data system, the resulting main Dynamic modes illustrate the different spatial structures associated with the wake vortex region and the near-wall region. By comparisons with proper orthogonal decomposition (POD), it can be concluded that the dynamic mode concentrates on a certain frequency component more effectively than the mode determined by POD. During the analysis, DMD has proven itself a robust and reliable algorithm to extract spatial-temporal coherent structures.展开更多
基金Project(12GJ6055)supported by the Natural Science Foundation of Hunan Province,ChinaProject(2010FJ4107)supported by Hunan Provincial Science and Technology Department,China
文摘The performances of repaired image depend on the local information in the repaired area and the consistency between the repair directions with structural content.Image repair algorithm with texture information performs well in repairing seriously damaged images,but it has bad performances when the images have the abundant structure information.The dual optimization image repair algorithm based on the linear structure and the optimal texture is proposed.The algorithm uses the double-constraint sparse model to reconstruct the missed information in large area in order to improve the clarity of repaired images.After adopting the preference of Criminisi priority,the image repair algorithm of self-similarity characteristics is proposed to improve the fault and fuzzy distortion phenomena in the repaired image.The results show that the proposed algorithm has more clarity in the image texture and structure and better effectiveness,and the peak signal-to-noise ratio of the repaired images by proposed algorithm is superior to that by other algorithms.
基金supported in part by the National Natural Science Foundation of China under Grant 61379143in part by the Fundamental Research Funds for the Central Universities under Grant 2015QNA66in part by the Qing Lan Project of Jiangsu Province
文摘Image enhancement is a popular technique,which is widely used to improve the visual quality of images.While image enhancement has been extensively investigated,the relevant quality assessment of enhanced images remains an open problem,which may hinder further development of enhancement techniques.In this paper,a no-reference quality metric for digitally enhanced images is proposed.Three kinds of features are extracted for characterizing the quality of enhanced images,including non-structural information,sharpness and naturalness.Specifically,a total of 42 perceptual features are extracted and used to train a support vector regression(SVR) model.Finally,the trained SVR model is used for predicting the quality of enhanced images.The performance of the proposed method is evaluated on several enhancement-related databases,including a new enhanced image database built by the authors.The experimental results demonstrate the efficiency and advantage of the proposed metric.
基金supported by the National Natural Science Foundation of China (Grant Nos. 10832001 and 10872145)the State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences
文摘We present dynamic mode decomposition (DMD) for studying the hairpin vortices generated by hemisphere protuberance measured by two-dimensional (2D) time-resolved (TR) particle image velocimetry (PIV) in a water channel. The hairpins dynamic information is extracted by identifying their dominant frequencies and associated spatial structures. For this quasi-periodic data system, the resulting main Dynamic modes illustrate the different spatial structures associated with the wake vortex region and the near-wall region. By comparisons with proper orthogonal decomposition (POD), it can be concluded that the dynamic mode concentrates on a certain frequency component more effectively than the mode determined by POD. During the analysis, DMD has proven itself a robust and reliable algorithm to extract spatial-temporal coherent structures.
