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Arumuga Perumal, S.
- A Novel Approach to Image Denoising by Combining Neighshrink and Sureshrink in Wavelet Domain
Authors
1 Department of Computer Science, Sadakathullah Appa College, Tirunelveli-627011, Tamilnadu, IN
2 Department of Computer Science, S. T. Hindu College, Nagarcoil-629002, Tamilnadu, IN
3 Centre for Information Technology and Engineering, Manonmanium Sundaranar University, Tirunelveli, Tamilnadu-627012, IN
4 Department of Electronics and Communication Engineering, Kamaraj College of Engineering and Technology, SPGC Nagar, Post Box No.12, Virudhunagar-626001, Tamilnadu, IN
Source
Digital Image Processing, Vol 2, No 2 (2010), Pagination: 60-67Abstract
Removing noise from the original image is still a challenging problem for researchers. A traditional way to remove noise from image data is to employ spatial filters. With wavelet transform gaining popularity in the last two decades, various algorithms for denoising in wavelet domain were introduced. In this paper, it is proposed to combine Neighshrink and Sureshrink to denoise an image corrupted by additive white Gaussian noise in wavelet domain.Keywords
Image Denoising, Dual Tree Discrete Wavelet Packet Transform, Root Mean Square Error, Peak Signal to Noise Ratio, Quality Index and Normalized Weighted Performance Metric.
- Area Level Fusion of Multi-Focused Images Using Dual Tree Complex Wavelet Packet Transform
Authors
1 Department of Electronics and Communication Engineering, Kamaraj College of Engineering and Technology, SPGC Nagar, Post Box No.12, Virudhunagar-626 001, Tamilnadu, IN
2 Department of Computer Science, St. Hindu Colleg, Nagarcoil–629 002, Tamilnadu, IN
3 Kamaraj College of Engineering and Technology, SPGC Nagar, Post Box No.12, Virudhunagar–626 001, Tamilnadu, IN
Source
Digital Image Processing, Vol 1, No 5 (2009), Pagination: 213-220Abstract
The fast development of digital image processing leads to the growth of feature extraction of images which leads to the development of Image fusion. Image fusion is defined as the process of combining two or more different images into a new single image retaining important features from each image with extended information content. There are two approaches to image fusion,namely spatial fusion and multi scale transform fusion. In spatial fusion, the pixel values from the source images are directly summed up and taken average to form the pixel of the composite image at thatlocation. Multi scale transform fusion uses transform for representing the source image at multi scale. The most common widely used transform for image fusion at multi scale is Discrete Wavelet Transform (DWT) since it minimizes structural distortions. But, wavelet transform suffers due to poor directionality and does not provide a geometrically oriented decomposition in multiple directions. One way to generalize the discrete wavelet transform so as to generate a structured dictionary of base is given by the Discrete Wavelet Packet Transform (DWPT). This benefit comes from the ability of the wavelet packets to better represent high frequency content and high frequency oscillating signals in particular. However, it is well known that both DWT and DWPT are shift varying. The Dual Tree Complex Wavelet Transform (DTCWT) introduced by Kingsbury, is approximately shift -invariant and provides directional analysis. And there are three levels for image fusion namely pixel level, area level and region level. In this paper, it is proposed to implement area level fusion of multi focused images using Dual Tree Complex Wavele Packet Transform (DTCWPT), extending the DTCWT as the DWPT extends the DWT and the performance is measured in terms of various performance measures like ischolar_main mean square error, peak signal to noise ratio, quality index and normalized weighted performance metric.
Keywords
Image fusion, Dual Tree Discrete Wavelet Packet Transform, Root Mean Square Error, Peak Signal to Noise Ratio, Quality Index and Normalized Weighted Performance Metric.- Area level fusion of Multi-focused Images using Double Density DWT and DTCWT
Authors
1 Department of Electronics and Communication Engineering, Kamaraj College of Engineering and Technology, SPGC Nagar, Post Box No.12, Virudhunagar–626 001, Tamilnadu, IN
2 Department of Computer Science, St. Hindu Colleg, Nagarcoil–629 002, Tamilnadu, IN
3 Kamaraj College of Engineering and Technology, SPGC Nagar, Post Box No.12, Virudhunagar–626 001, Tamilnadu, IN
Source
Digital Image Processing, Vol 1, No 6 (2009), Pagination: 231-242Abstract
Image fusion is a process of combining two or more different images into a new single image retaining important features from each image with extended information content. There are two approaches to image fusion, namely spatial fusion and multi scale transform fusion. In spatial fusion, the pixel values from the source images are directly summed up and taken average to form the pixel of the composite image at that location. Multi scale transform fusion usestransform for representing the source image at multi scale. The most common widely used transform for image fusion at multi scale is Discrete Wavelet Transform (DWT) since it minimizes structural distortions. But, wavelet transform cannot provide efficient approximation for directional features of images which in turn affects the performance of DWT-based image fusion schemes. Many multi scale tools have been invented to boost image fusion performance by incorporating directional representation. These tools can be classified into two categories according to the domain where they are designed: Spatial-domain Multiscale Directional Transform (SMDT) and Frequency domain Multiscale Directional Transform (FMDT). In FMDT, the basis functions of each subband orient at a certain direction, overcoming the poor directionality of 2-D DWT. Representative work includes curvelets, contourlets, bandelets, directionlets, multiscale directional filter banks, and complex wavelets. The critically sampled DWT is not a shift-invariant discrete transform, but the Dual Tree Complex Wavelet Transform (DT-CWT) introduced by Kingsbury is approximately shift -invariant and provides directional analysis whereas the undecimated DWT (UDWT) is an exactly shift-invariant transform. When J scales are implemented, the UDWT is expansive by the factor J + 1. The Double-density Discrete Wavelet Transform (DDWT) proposed by Ivan W. Selesnick provides a compromise between the UDWT and the critically-sampled DWT. A Double-density DTCWT (DDT-CWT), also proposed by Ivan W. Selesnick is an over-complete DWT designed to simultaneously possess the good properties of the DDWT and the DTCWT. And there are three levels for image fusion amel pixel level, area level and region level. In this paper, it is proposed to implement area level fusion of multi focused images using Double Density DWT and DTCWPT and the performance is measured in terms of various performance measures like ischolar_main mean square error and peak signal to noise ratio.