Image smoothing and sharpening based on nonlinear diffusion equation

Kong³

2014

IJMA

Section: Introductionmentioning

confidence: 99%

Boosting CED Using Robust Orientation Estimation

Khan¹,

Kong³

2014

IJMA

“…In the process of evaluation, the image got enhanced ridge pattern, as shown in Fig. 1 The diffusion matric 11 12…”

Section: Coherence Enhanced Diffusionmentioning

confidence: 99%

Coherence Enhancement Diffusion Using Robust Orientation Estimation

Computer Science &Amp; Information Technology ( CS &Amp; IT )

Alhalabi

Shahid

et al. 2014

In this paper, a new robust orientation estimation for Coherence Enhancement Diffusion (CED) is proposed. In CED, proper scale selection is very important as the gradient vector at that scale reflects the orientation of local ridge. For this purpose, a new scheme is proposed in which pre calculated orientation, by using orientation diffusion, is used to find the correct true local scale. From the experiments it is found that the proposed scheme is working much better in noisy environment as compared to the traditional Coherence Enhancement Diffusion.

“…An improved anisotropic diffusion was developed by Fang et al [22] which consists of two independent terms for smoothing of noisy background and sharpening of edge features. Yu et al [23] presented a kernal anisotropic diffusion incorporating a kernalized gradient operator |∇[Φf ]| in place of |∇f | in DCF in order to apply the diffusion process in small specified window of the pixels.…”

Section: Related Anisotropic Diffusion Meth-odsmentioning

confidence: 99%

“…Another important consideration while choosing z0 is to set the degrees of three input membership functions µ small (z0), µ medium (z 0 ) and µ large (z 0 ) as 0, 0.25 and 0.5 respectively as shown in Figure 3. Based on the above consideration, the optimal range of z0 which fulfils the need of desired degrees of membership functions is found to be [22,30]. We tested the diffusion result on 40% corrupted 'Lena' image by varying the values of z 0 ranging from 24 to 28 in terms of peak signal to noise ratio (PSNR) which shows the best PSNR performance at z0 = 27.…”

Section: Selection Of Z 0 and Tmentioning

confidence: 99%

Fuzzy based diffusion coefficient function in anisotropic diffusion for impulse noise removal

Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing

Arya

Pattanaik

2012

This paper provides the use of rule based fuzzy scheme to define a new diffusion coefficient function in anisotropic diffusion for impulse noise removal with edge preservation. This is achieved by expressing the small, medium and large labels of second order pixel differences in fuzzy format. An aggregated output membership function of percentage of noisiness is then obtained by selecting an optimal linguistic value of second order pixel difference during inference process. The pixels have been classified as homogeneous, edge and noisy pixels based on the degrees of noisiness of the output membership functions. To achieve desired smoothing of the impulse noisy images with homogeneous background, the new diffusion coefficient function in anisotropic diffusion is redefined to vary it in accordance with the degrees of noisiness of the output membership functions. The experimental results have been compared with existing anisotropic diffusion methods as well as advanced median filtering method. It is observed through experimental results that the proposed method works satisfactorily for images having impulsive noise density upto 50%. ...$15.00. pixels in image. This noise is generally termed as random valued impulsive noise (RVIN) [1] which affects the dynamic pixel luminance range of the image. If we denote the dynamic pixel luminance range of an observed image f as [pmin, pmax] i.e. pmin ≤ fij ≤ pmax where (i, j) is the pixel position in f . Then, the RVIN model is defined by the gray level of f at (i, j) as follows [1]: