A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Vasuki, S.
- Performance Comparison of Improved Wavelet Based Color Image Denoising Using Shrinkage Methods
Authors
1 Electronics and Communication Engineering Department, Velammal College of Engineering and Technology, Madurai, Tamil Nadu, IN
Source
Digital Image Processing, Vol 4, No 5 (2012), Pagination: 267-272Abstract
Removing noise from the original signal is still a challenging problem for researchers. There have been several algorithms and each approach has its assumptions, advantages, and limitations. This paper proposes an effective image denoising of color images in multiresolution transform domain using modified adaptive shrinkage. Most traditional noise reduction method tends to over-suppress high-frequency details. For overcoming this problem the input image is first decomposed into flat and edge regions. Noise is removed using the alpha map computed from wavelet transform coefficients of LH, HL, and HH bands. Noise is removed in the flat regions by Inner Product method. After removing noise in the flat regions, further noise removal is done in the edge regions using different types of wavelet shrinkage functions. Experimental results show that the NeighShrink can effectively reduce noise without losing sharp details in the noisy images and is suitable for commercial low-cost imaging systems.Keywords
Image Denoising, Noise Reduction, Shrinkage Functions, Wavelet Transform.
- An Improved Segmentation Algorithm for Textured Color Images Using Dual Tree Complex Wavelet Derived Features In Histogram Thresholding Techniques
Authors
1 Electronics and Communication Engineering Department, Velammal College of Engineering and Technology, Madurai, TamilNadu, IN
2 Computer Science & Engineering, A. C. College of Engineering and Technology, Karaikudi, TamilNadu, IN
Source
Digital Image Processing, Vol 1, No 2 (2009), Pagination: 62-67Abstract
This paper proposes an improved texture segmentation algorithm based on the features derived from Dual Tree Complex Wavelet Transform (DTCWT) which is proved to be efficient for texture description. The dual tree introduces limited redundancy, approximate shift invariance and directional selectivity while preserving perfect reconstruction and computational efficiency.DTCWT is applied on the three components of the input color image.Co occurrence features are computed for the resultant sub images.Then, the sub image which has the maximum energy is selected for which local homogeneity is calculated. Various histogram thresholding techniques are applied separately on the resultant homogeneity histogram. The experiments of segmentation provide more encouraging results for textured color images using peak finding algorithm than those based on Mean shift and Otsu multi thresholding algorithms. The results obtained using a set of real world colored textures demonstrated the usefulness of wavelet features in color texture image segmentation.