A feature-dependent fuzzy bidirectional flow for adaptive image sharpening

2010

Sci. China Inf. Sci.

Self Cite

A large number of applications in image processing and computer vision depend on image quality. In this paper, combining the forward diffusion with the backward diffusion by different weights, we present an adaptive bidirectional diffusion method for image denoising and deblurring simultaneously. Further, we introduce a gradient factor into the data fidelity term, which forms a spatially varying constraint and allows a better restoration of image edges and fine details. In order to obtain a stable solution, we develop a numerically stable scheme and give its theoretical analysis. Finally, we show advantages of this method in deblurring edges, denoising and restoring fine details of image compared with other related methods in experiments.

Section: Resultsmentioning

confidence: 99%

“…Since then a large number of nonlinear edge preserving diffusion equations have been presented for image denoising, edge detection, image restoration and segmentation, etc. [8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Adaptive bidirectional diffusion for image restoration

2010

Sci. China Inf. Sci.

Self Cite

“…For the edge between different objects, a shock-type backward diffusion is performed in the normal direction, incorporating a forward diffusion in the tangent direction. For the texture and detail, the shock filter with the sign function enhances image features in a binary decision process, which produces unfortunately a false piecewise constant result [29,36]. To overcome this drawback, we use a hyperbolic tangent function to control softly the change of gray levels of the image.…”

Section: Our Methodsmentioning

confidence: 99%

“…Its idea follows the shock wave calculation in computational fluid mechanics. Further improvements have been done subsequently in [26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Image denoising and deblurring: non-convex regularization, inverse diffusion and shock filter

2011

Sci. China Inf. Sci.

Self Cite

A large number of applications in image processing and computer vision depend on image quality. In this paper, main concerns are image denoising and deblurring simultaneously in a restoration task by three types of methodologies: non-convex regularization, inverse diffusion and shock filter. We discuss their relations in the context of image deblurring: the inverse diffusion implied by the non-convex regularization, and the superior ability of deblurring edge of the shock filter to that of the inverse diffusion, both in 1D and 2D cases. Finally, we propose a region-based adaptive anisotropic diffusion with shock filter method, which shows advantages of deblurring edges, denoising and smoothing contours in experiments, compared with some related methods. Therein an idea of "divide and rule" is introduced.

“…Then, Osher and Rudin introduced a novel edge sharpening technique called shock filter. 5 In order to avoid unnatural artifacts of the shock-type processing, 10 we introduced a soft edge sharpening algorithm in a coupled bidirectional diffusion (CBD) equation:…”

Section: Other Related Workmentioning

confidence: 99%

Adaptive mixed image denoising based on image decomposition

2011

Opt. Eng

Abstract. Image denoising while preserving image features is a key problem in image processing and computer vision. This letter proposes an adaptive mixed method for image restoration. First, this method decomposes a given image as the sum of two components: geometric structure and oscillating pattern according to Meyer's theory. Second, a coupled bidirectional diffusion equation is used to restore the structure part, and a nonlocal means filter is used to remove noise in the oscillating part. Experimental results show advantages of this method in feature-preserving denoising. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).