scholarly journals Degraded Image Enhancement Using Dual-Domain-Adaptive Wavelet and Improved Fuzzy Transform

2020 ◽  
Author(s):  
Yuehua Huo ◽  
Weiqiang Fan ◽  
Xiaoyu Li
Keyword(s):  
Performance Evaluation ◽  
Image Enhancement ◽  
High Frequency ◽  
Low Frequency ◽  
Original Image ◽  
Fuzzy Transform ◽  
Adaptive Wavelet ◽  
Comprehensive Performance ◽  
Degraded Image ◽  
Dual Domain

Abstract A novel enhancement algorithm of degraded image based on dual-domain-adaptive wavelet and improved fuzzy transform is proposed, aiming at the problem of surveillance videos degradation caused by complex lighting conditions underground. The dual-domain filtering (DDF) is used to decompose the image into low-frequency sub-image and high-frequency sub-images. The contrast limited adaptive histogram enhancement (CLAHE) is used to adjust the overall brightness and contrast of the low-frequency sub-image. Discrete wavelet transform (DWT) is used to obtain low frequency sub-band (LFS) and high frequency sub-band (HFS). The wavelet shrinkage threshold method based on Bayesian estimation is used to calculate the wavelet threshold corresponding to the HFS at different scales. A Garrate threshold function that introduces adaptive adjustment factor and enhancement coefficient is designed to adaptively de-noise and enhance the HFS coefficients corresponding to wavelet thresholds at different scales. Meanwhile, the gamma function is used to realize the correction of the LFS coefficients. The constructed PAL fuzzy enhancement operator is used to perform contrast enhancement and highlight area suppression on the reconstructed image to obtain an enhanced image. The proposed algorithm is evaluated by subjective vision and objective indicators. The experimental results show that the proposed algorithm can significantly improve the overall brightness and contrast of the original image, suppress noise of dust & spray, enhance the image details and improve the visual effect of the original image. Compared with the images enhanced by the STFE, GTFE, CLAHE, SSR, MSR, DGR, and MSWT algorithms, the comprehensive performance evaluation indicators of the images enhanced by the proposed algorithm are increased by 312.50%, 34.69%, 53.49%, 22.22%, 32.00%, 10.00%, 60.98%, 3.13%, respectively. At the same time, comprehensive performance evaluation indicator of the enhance image and the robustness is the best, which is more suitable for image enhancement in different mine environments.

scholarly journals Degraded Image Enhancement Using Dual-Domain-Adaptive Wavelet and Improved Fuzzy Transform

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Weiqiang Fan ◽  
Yuehua Huo ◽  
Xiaoyu Li
Keyword(s):  
Image Enhancement ◽  
Low Frequency ◽  
Threshold Function ◽  
Discrete Wavelet ◽  
Fuzzy Transform ◽  
Adaptive Wavelet ◽  
Fuzzy Enhancement ◽  
Lighting Conditions ◽  
Degraded Image ◽  
Dual Domain

A novel enhancement algorithm for degraded image using dual-domain-adaptive wavelet and improved fuzzy transform is proposed, aiming at the problem of surveillance videos degradation caused by the complex lighting conditions underground coal mine. Firstly, the dual-domain filtering (DDF) is used to decompose the image into base image and detail image, and the contrast limited adaptive histogram enhancement (CLAHE) is adopted to adjust the overall brightness and contrast of the base image. Then, the discrete wavelet transform (DWT) is utilized to obtain the low frequency sub-band (LFS) and high frequency sub-band (HFS). Next, the wavelet shrinkage threshold is applied to calculate the wavelet threshold corresponding to the HFS at different scales. Meanwhile, a new Garrate threshold function that introduces adjustment factor and enhancement coefficient is designed to adaptively de-noise and enhance the HFS coefficients, and the Gamma function is employed to correct the LFS coefficients. Finally, the PAL fuzzy enhancement operator is improved and used to perform contrast enhancement and highlight area suppression on the reconstructed image to obtain an enhanced image. Experimental results show that the proposed algorithm can not only significantly improve the overall brightness and contrast of the degraded image but also suppresses the noise of dust and spray and enhances the image details. Compared with the similar algorithms of STFE, GTFE, CLAHE, SSR, MSR, DGR, and MSWT algorithms, the indicator values of comprehensive performance of the proposed algorithm are increased by 205%, 195%, 200%, 185%, 185%, 85%, 140%, and 215%, respectively. Moreover, compared with the other seven algorithms, the proposed algorithm has strong robustness and is more suitable for image enhancement in different mine environments.

A remote sensing image enhancement method using mean filter and unsharp masking in non-subsampled contourlet transform domain

2016 ◽  
Vol 39 (2) ◽  
pp. 183-193 ◽  
Author(s):  
Lu Liu ◽  
Zhenhong Jia ◽  
Jie Yang ◽  
Nikola Kasabov
Keyword(s):  
Remote Sensing ◽  
Image Enhancement ◽  
High Frequency ◽  
Low Frequency ◽  
Remote Sensing Image ◽  
Contourlet Transform ◽  
Inverse Transformation ◽  
Unsharp Masking ◽  
Mean Filter ◽  
Enhancement Method

The intelligibility of an image can be influenced by the pseudo-Gibbs phenomenon, a small dynamic range, low-contrast, blurred edge and noise pollution that occurs in the process of image enhancement. A new remote sensing image enhancement method using mean filter and unsharp masking methods based on non-subsampled contourlet transform (NSCT) in the scope for greyscale images is proposed in this paper. First, the initial image is decomposed into the NSCT domain with a low-frequency sub-band and several high-frequency sub-bands. Secondly, linear transformation is adopted for the coefficients of the low-frequency sub-band. The mean filter is used for the coefficients of the first high-frequency sub-band. Then, all sub-bands were reconstructed into spatial domains using the inverse transformation of NSCT. Finally, unsharp masking was used to enhance the details of the reconstructed image. The experimental results show that the proposed method is superior to other methods in improving image definition, image contrast and enhancing image edges.

scholarly journals Fast Texture Synthesis in Adaptive Wavelet Packet Trees

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Ying-Shen Juang ◽  
Hsi-Chin Hsin ◽  
Tze-Yun Sung ◽  
Carlo Cattani
Keyword(s):  
High Frequency ◽  
Texture Synthesis ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Computation Time ◽  
Wavelet Packet Transform ◽  
Synthesis Process ◽  
Adaptive Wavelet ◽  
Hierarchical Trees ◽  
Speed Up

Wavelet packet transform known as a substantial extension of wavelet transform has drawn a lot of attention to visual applications. In this paper, we advocate using adaptive wavelet packet transform for texture synthesis. The adaptive wavelet packet coefficients of an image are organized into hierarchical trees called adaptive wavelet packet trees, based on which an efficient algorithm has been proposed to speed up the synthesis process, from the low-frequency tree nodes representing the global characteristics of textures to the high-frequency tree nodes representing the local details. Experimental results show that the texture synthesis in the adaptive wavelet packet trees (TSIAWPT) algorithm is suitable for a variety of textures and is preferable in terms of computation time.

Remote Sensing Image Enhancement Based on Wavelet Transformation

2012 ◽  
Vol 198-199 ◽  
pp. 223-226
Author(s):  
Ying Zhao ◽  
Ye Cai Guo
Keyword(s):  
Remote Sensing ◽  
Wavelet Transform ◽  
Theoretical Analysis ◽  
High Frequency ◽  
Wavelet Transformation ◽  
Low Frequency ◽  
Remote Sensing Image ◽  
Original Image ◽  
Remote Sensing Images ◽  
Enhancement Algorithm

The contrast of remote sensing images is very low, which include various noises. In order to make full used of remote sensing image information extraction and processing, the original image should have to be enhanced. In this paper the enhancement algorithm based on the biothogonal wavelet transform is proposed. Firstly, we have to eliminate the beforehand noise, and then take advantage of the non-linear wavelet transform to enhanced low-frequency and high- frequency coefficient respectively. Finally, the new picture is reconstruct by the transformed low-frequency and high-frequency coefficient. The efficiency of the proposed algorithm was proved by the theoretical analysis and computer simulations.

The Research of XLPE Cable Comprehensive Performance Evaluation Based on VLF Technology

2017 ◽  
Author(s):  
Gao Xuan ◽  
Zheng Zheng ◽  
Zhang Yizhou ◽  
Wang Liang ◽  
Yun Hao ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Nuclear Power ◽  
Low Frequency ◽  
Power Cables ◽  
Insulation Resistance ◽  
Aging Condition ◽  
Power Station ◽  
Xlpe Cable ◽  
Very Low Frequency ◽  
Comprehensive Performance

Very low frequency (VLF) technology can effectively evaluate the damp cable aging condition, and IEEE 400.2 formulate the dielectric diagnostic indicators and standards of the VLF technology, but this technology is not popular in Chinese nuclear power station. The paper has carried on 0.1Hz VLF dielectric loss test, insulation resistance test on three 10kV XLPE power cables which have same specifications but different operation environment. Combined those electric test with the cable history environment information, the result shows it’s very useful for the cable comprehensive performance evaluation.

NSCT-Based Image Enhancement Combing Homomorphic Filtering with Fuzzy Theory

2014 ◽  
Vol 989-994 ◽  
pp. 4136-4139
Author(s):  
Kai Wen ◽  
Bin Sun ◽  
Jing Yang
Keyword(s):  
Fuzzy Sets ◽  
Image Enhancement ◽  
High Frequency ◽  
Fuzzy Theory ◽  
Low Frequency ◽  
Fuzzy Sets Theory ◽  
Inverse Transform ◽  
Homomorphic Filtering ◽  
Image Edge ◽  
Sets Theory

Proposed is an algorithm of NSCT-Based Image Enhancement Combing Homomorphic Filtering With Fuzzy theory to improve the performance of traditional algorithms in the expression of the image edge detail and contrast. With the NSCT, both the low and high frequency sub-band components can be extracted. We dispose of the low frequency sub-band components of the decomposed image by homomorphic filtering and enhance the high-frequency sub-band components with fuzzy sets theory. Then the reconstructed image after above enhancement is obtained by using NSCT inverse transform.

scholarly journals Method of Improved Fuzzy Contrast Combined Adaptive Threshold in NSCT for Medical Image Enhancement

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Fei Zhou ◽  
ZhenHong Jia ◽  
Jie Yang ◽  
Nikola Kasabov
Keyword(s):  
Image Enhancement ◽  
Fuzzy Set ◽  
High Frequency ◽  
Medical Image ◽  
Medical Images ◽  
Low Frequency ◽  
Noise Removal ◽  
Adaptive Threshold ◽  
Image Noise ◽  
Medical Image Enhancement

Noises and artifacts are introduced to medical images due to acquisition techniques and systems. This interference leads to low contrast and distortion in images, which not only impacts the effectiveness of the medical image but also seriously affects the clinical diagnoses. This paper proposes an algorithm for medical image enhancement based on the nonsubsampled contourlet transform (NSCT), which combines adaptive threshold and an improved fuzzy set. First, the original image is decomposed into the NSCT domain with a low-frequency subband and several high-frequency subbands. Then, a linear transformation is adopted for the coefficients of the low-frequency component. An adaptive threshold method is used for the removal of high-frequency image noise. Finally, the improved fuzzy set is used to enhance the global contrast and the Laplace operator is used to enhance the details of the medical images. Experiments and simulation results show that the proposed method is superior to existing methods of image noise removal, improves the contrast of the image significantly, and obtains a better visual effect.

scholarly journals A Novel Image Authentication with Tamper Localization and Self-Recovery in Encrypted Domain Based on Compressive Sensing

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Rui Zhang ◽  
Di Xiao ◽  
Yanting Chang
Keyword(s):  
Compressive Sensing ◽  
High Frequency ◽  
Low Frequency ◽  
Image Transmission ◽  
Image Authentication ◽  
Tamper Detection ◽  
Discrete Wavelet ◽  
Original Image ◽  
High Frequency Part ◽  
Tamper Recovery

This paper proposes a novel tamper detection, localization, and recovery scheme for encrypted images with Discrete Wavelet Transformation (DWT) and Compressive Sensing (CS). The original image is first transformed into DWT domain and divided into important part, that is, low-frequency part, and unimportant part, that is, high-frequency part. For low-frequency part contains the main information of image, traditional chaotic encryption is employed. Then, high-frequency part is encrypted with CS to vacate space for watermark. The scheme takes the processed original image content as watermark, from which the characteristic digest values are generated. Comparing with the existing image authentication algorithms, the proposed scheme can realize not only tamper detection and localization but also tamper recovery. Moreover, tamper recovery is based on block division and the recovery accuracy varies with the contents that are possibly tampered. If either the watermark or low-frequency part is tampered, the recovery accuracy is 100%. The experimental results show that the scheme can not only distinguish the type of tamper and find the tampered blocks but also recover the main information of the original image. With great robustness and security, the scheme can adequately meet the need of secure image transmission under unreliable conditions.

BLIND IMAGE RESTORATION BY COMBINING WAVELET TRANSFORM AND RBF NEURAL NETWORK

2007 ◽  
Vol 05 (01) ◽  
pp. 15-26 ◽  
Author(s):  
PING GUO ◽  
HONGZHAI LI ◽  
MICHAEL R. LYU
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
High Frequency ◽  
Additive Noise ◽  
Rbf Neural Network ◽  
Low Frequency ◽  
Degraded Image ◽  
Speed Up ◽  
Blind Image ◽  
The Given

In this paper, we present a novel technique for restoring a blurred noisy image without any prior knowledge of the blurring function and the statistics of noise. The technique combines wavelet transform with radial basis function (RBF) neural network to restore the given image which is degraded by Gaussian blur and additive noise. In the proposed technique, the wavelet transform is adopted to decompose the degraded image into high frequency parts and low frequency part. Then the RBF neural network based technique is used to restore the underlying image from the given image. The inverse principal element method (IPEM) is applied to speed up the computation. Experimental results show that the proposed technique inherited the advantages of wavelet transform and IPEM, and the algorithm is efficient in computation and robust to the noise.

scholarly journals Compressed Sensing for Image Compression Using Wavelet Packet Analysis

2013 ◽  
pp. 16-20
Author(s):  
Kanike Vijay Kumar ◽  
K. Suresh Reddy
Keyword(s):  
Compressed Sensing ◽  
Image Compression ◽  
High Frequency ◽  
Matching Pursuit ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Original Image ◽  
Optimal Basis ◽  
Naturally Occurring ◽  
Random Measurements

Compressed sensing is a recently developed technique that exploits the sparsity of naturally occurring signals and images to reduce the volume of the data using less number of samples, computing the sparsity of the signal. In the traditional/conventional approaches the images are acquired and compressed, where as compressed sensing aims to acquire the “compressed signals” with few numbers of samples and reconstruct the images. This will allow us to acquire the large ground/region with few numbers of input samples. This technique works on the assumption that natural signals/images have inherent sparsity. . In this algorithm, the original image is first decomposes with the wavelet packet to make it sparse, and then retains the low frequency coefficients in line with the optimal basis of the wavelet packet, meanwhile, makes random measurements of all the high frequency coefficients according to the compressed sensing theory, and last restores them with the orthogonal matching pursuit (OMP) method, and does the inverse transform of the wavelet packet to reconstruct the original image, to achieve the image compression.

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