Abstract
To eliminate heavy noise and retain more scene details, we propose a structure-oriented total variation (TV) model based on data dependent kernel function and TV criterion for image denoising application. The innovative model introduces the weights produced from the local and nonlocal symmetry features involved in the image itself to pick more precise solutions in the TV denoising process. As a result, the proposed local and nonlocal steering kernel weighted TV model yields excellent noise suppression and structure-preserving performance. The experimental results verify the validity of the proposed model in objective quantitative indices and subjective visual appearance.
Highlights
Image denoising is a vital preprocessing step for image based object detection, recognition, and tracking [1,2,3,4,5,6]
The bilateral total variation (BTV) model [18] and non-local total variation (NLTV) model [19] are successively exploited to more precisely restore the details by fusing the idea of bilateral filtering and non-local means filtering [20] with TV criterion
Our proposed local and nonlocal steering kernel weighted TV model for image denoising can robustly estimate the local structure of the image, as well as effectively remove the annoying noise
Summary
Image denoising is a vital preprocessing step for image based object detection, recognition, and tracking [1,2,3,4,5,6]. With the local constraints of LSK to weight the respective measurements of TV, which enhances the structure preserving capability of the TV model in denoising application. In this way, our proposed local and nonlocal steering kernel weighted TV model for image denoising can robustly estimate the local structure of the image, as well as effectively remove the annoying noise. Our proposed local and nonlocal steering kernel weighted TV model for image denoising can robustly estimate the local structure of the image, as well as effectively remove the annoying noise It utilizes the redundancy of symmetrically similar patches in the corrupted image and the sensitivity of local feature description to implement the denoising task.
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