Abstract
We propose an adaptive total variation (TV) model by introducing the steerable filter into the TV-based diffusion process for image filtering. The local energy measured by the steerable filter can effectively characterize the object edges and ramp regions and guide the TV-based diffusion process so that the new model behaves like the TV model at edges and leads to linear diffusion in flat and ramp regions. This way, the proposed model can provide a better image processing tool which enables noise removal, edge-preserving, and staircase suppression.
Highlights
Image denoising is a fundamental task in the community of image processing, but there is always a dilemma for the denoising algorithms to simultaneously remove noise and to preserve edges
Nine well-known models are employed for comparison, including the total variation (TV) model [14], the PM model [8], YK model [23], the ATV model [29], the DED model [9], the Hajiaboli’s fourth-order model (Hajiaboli) [24], the LARK [1], the BM3D [5], and the nonlocal mean (NLM) [3]
We have proposed a nonlinear diffusion process (STV) for image filtering
Summary
Image denoising is a fundamental task in the community of image processing, but there is always a dilemma for the denoising algorithms to simultaneously remove noise and to preserve edges. The Gaussian filter can smooth noise effectively, but it blurs the edges since it is just a low-pass filter which cannot discern noise and edges Recent advances on this topic have brought considerable improvement in denoising performance, such as the kernel regression (LARK) [1], bilateral filter [2], patch-based method [3, 4], BM3D [5], and the partial differential equation (PDE) based methods [6]. One typical algorithm is the anisotropic diffusion proposed by Perona and Malik (PM model) [8], which is able to reduce diffusion amount around boundaries and achieve a good trade-off between noise removal and edge preservation. The total variation model is another typical model which minimizes the following functional [14]: ETV
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
