Speckle noise reduction in ultrasound images using a discrete wavelet transform-based image fusion technique

2015 ◽  
Vol 26 (s1) ◽  
pp. S1587-S1597 ◽  
Author(s):  
Hyun Ho Choi ◽  
Ju Hwan Lee ◽  
Sung Min Kim ◽  
Sung Yun Park
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Noise Reduction ◽  
Discrete Wavelet Transform ◽  
Speckle Noise ◽  
Discrete Wavelet ◽  
Ultrasound Images ◽  
Fusion Technique ◽  
Image Fusion Technique ◽  
Speckle Noise Reduction

scholarly journals Performance of Spatial Frequency Discrete Wavelet Transform Based Remote Sensing Image Fusion Algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 7968-7978
Keyword(s):  
Remote Sensing ◽  
Wavelet Transform ◽  
Spatial Frequency ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Fusion Algorithm ◽  
Fusion Technique ◽  
Image Fusion Technique ◽  
Better Than

The high sensor cost for producing images with superior spectral and spatial qualities in remote sensing application have led to the development of image fusion algorithms. Image fusion technique combines a Panchromatic image and a Multispectral image with an aim to produce images with excellent spatial and spectral qualities. One of the major factors that affect the performance of any image fusion algorithm is the capability of the algorithm in extracting the spatial and spectral data from the respective images and how effective the so extracted information is blended together. One of the recently developed spectral domain algorithm to perform image fusion in remote sensing applications is Spatial Frequency Discrete Wavelet Transform abbreviated as SFDWT. The excellence of SFDWT image fusion algorithm is already proven better than the prevailing algorithms based on Discrete Wavelet Transform. This paper is coined with an eye to realize the performance of SFDWT based image fusion algorithm with respect to IHS-DWT, which being an enhanced form of a typical DWT based image fusion algorithm. The performance of SFDWT and IHS-DWT based image fusion algorithms will be evaluated by applying both techniques in the fusion of urban images received from Pléiades sensors with 1:4 resolution ratio using qualitative and quantitative image quality assessment methods. The consequence of varying the decomposition level on the quality of the images produced using SFDWT image fusion technique and three variants of IHS-DWT techniques based on substitution, averaging and maximum selection will be also evaluated. From the experimental analysis done using MATLAB simulation, it will be vivid that images obtained using image fusion algorithm based on SFDWT are much better than that obtained using IHS-DWT technique with excellent spatial and spectral qualities

scholarly journals Speckle Noise Reduction Technique for SAR Images Using Statistical Characteristics of Speckle Noise and Discrete Wavelet Transform

2019 ◽  
Vol 11 (10) ◽  
pp. 1184 ◽  
Author(s):  
Hyunho Choi ◽  
Jechang Jeong
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Multiplicative Noise ◽  
Additive Noise ◽  
Low Frequency ◽  
Speckle Noise ◽  
Discrete Wavelet ◽  
Sar Images ◽  
Soft Threshold ◽  
Speckle Noise Reduction

Synthetic aperture radar (SAR) images map Earth’s surface at high resolution, regardless of the weather conditions or sunshine phenomena. Therefore, SAR images have applications in various fields. Speckle noise, which has the characteristic of multiplicative noise, degrades the image quality of SAR images, which causes information loss. This study proposes a speckle noise reduction algorithm while using the speckle reducing anisotropic diffusion (SRAD) filter, discrete wavelet transform (DWT), soft threshold, improved guided filter (IGF), and guided filter (GF), with the aim of removing speckle noise. First, the SRAD filter is applied to the SAR images, and a logarithmic transform is used to convert multiplicative noise in the resulting SRAD image into additive noise. A two-level DWT is used to divide the resulting SRAD image into one low-frequency and six high-frequency sub-band images. To remove the additive noise and preserve edge information, horizontal and vertical sub-band images employ the soft threshold; the diagonal sub-band images employ the IGF; while, the low- frequency sub-band image removes additive noise using the GF. The experiments used both standard and real SAR images. The experimental results reveal that the proposed method, in comparison to state-of-the art methods, obtains excellent speckle noise removal, while preserving the edges and maintaining low computational complexity.

scholarly journals Multi-focus Image Fusion Using an Effective Discrete Wavelet Transform Based Algorithm

2014 ◽  
Vol 14 (2) ◽  
pp. 102-108 ◽  
Author(s):  
Yong Yang ◽  
Shuying Huang ◽  
Junfeng Gao ◽  
Zhongsheng Qian
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Low Frequency ◽  
Objective Evaluation ◽  
Discrete Wavelet ◽  
Evaluation Indexes ◽  
Fused Image ◽  
Focus Image

Abstract In this paper, by considering the main objective of multi-focus image fusion and the physical meaning of wavelet coefficients, a discrete wavelet transform (DWT) based fusion technique with a novel coefficients selection algorithm is presented. After the source images are decomposed by DWT, two different window-based fusion rules are separately employed to combine the low frequency and high frequency coefficients. In the method, the coefficients in the low frequency domain with maximum sharpness focus measure are selected as coefficients of the fused image, and a maximum neighboring energy based fusion scheme is proposed to select high frequency sub-bands coefficients. In order to guarantee the homogeneity of the resultant fused image, a consistency verification procedure is applied to the combined coefficients. The performance assessment of the proposed method was conducted in both synthetic and real multi-focus images. Experimental results demonstrate that the proposed method can achieve better visual quality and objective evaluation indexes than several existing fusion methods, thus being an effective multi-focus image fusion method.

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