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Akila, C.
- Fuzzy Filtering for Restoration of Color Images by Reducing Gaussian and Impulse Noise
Abstract Views :147 |
PDF Views:2
Authors
Affiliations
1 Anna University of Technology, Tirunelveli, IN
2 Department of CSE, Anna University, Tirunelveli, IN
1 Anna University of Technology, Tirunelveli, IN
2 Department of CSE, Anna University, Tirunelveli, IN
Source
Digital Image Processing, Vol 3, No 9 (2011), Pagination: 557-562Abstract
Image restoration is the process of recovering high quality original image from the degraded version of the image. The noises in the digital images are introduced during their acquisition and transmission. The objective of this paper is to denoise the color images which are affected by the Gaussian and Impulse noise. In this paper, the fuzzy peer cluster concept is used. A fuzzy peer cluster will be defined as a group of pixels which are similar to the processing pixel. The fuzzy peer cluster for each image pixel will be determined and the Fuzzy rule is used to detect the impulse noise in each image pixel. Impulse noise in the image pixel is removed by using the Swapping Bilateral Filter. Gaussian noise in the pixel is detected by means of the suggested median value. Gaussian noise in the image is reduced by using the same Swapping Bilateral Filter.Keywords
Fuzzy Technique, Image Noise, Image Restoration, Suggested Median, Swapping Bilateral Filter.- Image Denoising Using Principal Neighborhood Dictionary Non Local Means for Color Images
Abstract Views :183 |
PDF Views:6
Authors
Affiliations
1 Department of CSE, Anna University of Technology, Tirunelveli, IN
1 Department of CSE, Anna University of Technology, Tirunelveli, IN
Source
Digital Image Processing, Vol 3, No 4 (2011), Pagination: 216-221Abstract
This Paper presents an appealing loom to Non Local Means (NLM) image denoising algorithm that uses Principal Component Analysis (PCA) for dimensionality reduction. This Principal Component Analysis is applied individually for the three bands namely red, green, blue of the color images crooked with a frequently occurring noise model namely the Gaussian noise. As a result of PCA, obtain the Eigen value measures on the observed data and then develop a small number of artificial data set (principal components) using Parallel Analysis that will account for most of the variance in the observed data set. The principal components can then be used as predictor or criterion variables in subsequent analysis. Consequently measure the neighborhood similarity weight for the subspace using the normalization technique. The ensuing algorithm is referred to as the Principal Neighborhood Dictionary (PND) Nonlocal Means. The performance and the computational time of the proposed method is improved by the retrieval of the significant principal components from the projected subspace for each bands and increase of subspace window size respectively. The accuracy of the proposed PND method is compared with the other state of art methods and the superior performance of the proposed image denoising method is stated in terms of the increased Peak Signal to Noise Ratio (PSNR).Keywords
Nonlocal Means (NLM), Parallel Analysis, Principal Component Analysis, Principal Neighborhood Dictionary.- Texture Classification Using Wavelet Packet Decomposition Based on SGS & MISS Algorithm
Abstract Views :159 |
PDF Views:2
Authors
Affiliations
1 Anna University, Tirunelveli, IN
2 Department in CSE, Anna University, Tirunelveli, IN
3 PSNCET, Tirunelveli, IN
1 Anna University, Tirunelveli, IN
2 Department in CSE, Anna University, Tirunelveli, IN
3 PSNCET, Tirunelveli, IN