scholarly journals Image Representation Method Based on Complex Wavelet Transform and Phase Congruency, with Automatic Threshold Selection

2021 ◽  
Vol 15 ◽  
pp. 79-83
Author(s):  
T. Arathi ◽  
Latha Parameswaran
Keyword(s):  
Wavelet Transform ◽  
Image Quality ◽  
Image Representation ◽  
Adaptive Method ◽  
Input Image ◽  
Phase Congruency ◽  
Complex Wavelet Transform ◽  
Complex Wavelet ◽  
Representation Method

Image representation is an active area of research with increasing applications in military and defense. Image representation aims at representing an image with lesser number of coefficients than the actual image, without affecting the image quality. It is the first step in image compression. Once the image is represented by using some set of coefficients, it is further encoded using various compression algorithms. This paper proposes an adaptive method for image representation, which uses Complex Wavelet transform and the concept of phase congruency, where the number of coefficients used for image representation depends on the information content in the input image. The efficiency of the proposed method has been assessed by comparing the number of coefficients used to represent the image using the proposed method with that used when Complex Wavelet transform is used for image representation. The resultant image quality is determined by computing the PSNR values and Normalized Cross Correlation. Experiments carried out show highly promising results, in terms of the reduction in the number of coefficients used for image representation and the quality of the resultant image.

DAUBECHIES COMPLEX WAVELET TRANSFORM BASED TECHNIQUE FOR DENOISING OF MEDICAL IMAGES

2007 ◽  
Vol 07 (04) ◽  
pp. 663-687 ◽  
Author(s):  
ASHISH KHARE ◽  
UMA SHANKER TIWARY
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Medical Image ◽  
Medical Images ◽  
Universal Method ◽  
Complex Wavelet Transform ◽  
Shrinkage Function ◽  
Daubechies Complex Wavelet Transform ◽  
Complex Wavelet

Wavelet based denoising is an effective way to improve the quality of images. Various methods have been proposed for denoising using real-valued wavelet transform. Complex valued wavelets exist but are rarely used. The complex wavelet transform provides phase information and it is shift invariant in nature. In medical image denoising, both removal of phase incoherency as well as maintaining the phase coherency are needed. This paper is an attempt to explore and apply the complex Daubechies wavelet transform for medical image denoising. We have proposed a method to compute a complex threshold, which does not depend on any assumed model of noise. In this sense this is a "universal" method. The proposed complex-domain shrinkage function depends on mean, variance and median of wavelet coefficients. To test the effectiveness of the proposed method, we have computed the input and output SNR and PSNR of various types of medical images. The method gives an improvement for Gaussian additive, Speckle and Salt-&-Pepper noise as well as for the mixture of these noise types for a range of noisy images with 15 db to 30 db noise levels and outperforms other real-valued wavelet transform based methods. The application of the proposed method to Ultrasound, X-ray and MRI images is demonstrated in the experiments.

scholarly journals Reduced-reference image quality metric based on statistic model in complex wavelet transform domain

2019 ◽  
Vol 74 ◽  
pp. 218-230
Author(s):  
Xinwen Xie ◽  
Philippe Carré ◽  
Clency Perrine ◽  
Yannis Pousset ◽  
Nanrun Zhou ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Image Quality ◽  
Reference Image ◽  
Transform Domain ◽  
Complex Wavelet Transform ◽  
Quality Metric ◽  
Statistic Model ◽  
Image Quality Metric ◽  
Complex Wavelet

scholarly journals An Efficient Pass Parallel SPIHT based Image Compression using Double Density Dual Tree Complex Wavelet Transform for WSN

2019 ◽  
Vol 8 (12) ◽  
pp. 2762-2768
Keyword(s):  
Wavelet Transform ◽  
Image Quality ◽  
Image Compression ◽  
Wavelet Transforms ◽  
Computation Time ◽  
Set Partitioning ◽  
Complex Wavelet Transform ◽  
Compression Technique ◽  
Compression Algorithms ◽  
Complex Wavelet

For the past two decades, wavelet based image compression algorithms for Wireless Sensor Network (WSN) has gained broad attention than that of the spatial based image compression algorithms. In that, Dual Tree Complex Wavelet Transforms (DTCWT) has provided better results in terms of image quality and high compression rate. However, the selection of DTCWT based image compressions for various WSN based applications is not practically suitable, due to the major limitations of WSN such as, low bandwidth, low energy consumption and storage space. Therefore, an attempt has been made in this paper to develop image compression through simulation by considering the modified block based pass parallel Set Partitioning In Hierarchical Trees (SPIHT) with Double Density Dual Tree Complex Wavelet Transform (DDDTCWT) for compressing the WSN based images. In addition, bivariate shrink method is also adopted with the DDDTCWT to obtain better image quality within less computation time. It is observed through simulation results that above mentioned proposed technique provides better performance than that of existing compression technique

An Improved RNAMC Image Representation

2011 ◽  
Vol 143-144 ◽  
pp. 746-749
Author(s):  
Yun Ping Zheng ◽  
Zu Jia Li ◽  
Mudar Sarem ◽  
Qing Hong Yang ◽  
Xiu Xiu Liao
Keyword(s):  
Image Quality ◽  
Image Representation ◽  
Experimental Results ◽  
Reconstructed Image ◽  
Bit Rate ◽  
Gray Image ◽  
Packing Model ◽  
Representation Method ◽  
Improved Algorithm

In this paper, by controlling the ratio of the length and the width of a homogenous block, we proposed an improved algorithm for the gray image representation by using the Rectangular Non-symmetry and Anti-packing Model Coding (RNAMC) and extended shading approach, which is called the IRNAMC image representation method. Also, we present an IRNAMC representation algorithm of gray images. By comparing our proposed IRNAMC method with the conventional S-Tree Coding (STC) method, the experimental results presented in this paper show that the former can significantly reduce the lower bit rate and the number of homogenous blocks than the latter whereas remaining the satisfactory image quality. Also, the experimental results show that by controlling the ratio of the length and the width, we can improve the reconstructed image quality of the RNAMC method.

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