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.

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.

Underwater Image Enhancement Method Combining color and Luminance

2020 ◽  
Vol 13 ◽  
Author(s):  
ZHAO Baiting ◽  
WANG Feng ◽  
JIA Xiaofen ◽  
GUO Yongcun ◽  
WANG Chengjun
Keyword(s):  
Image Enhancement ◽  
Color Space ◽  
Low Frequency ◽  
Brightness Enhancement ◽  
Color Distortion ◽  
Underwater Image ◽  
Enhancement Method ◽  
Frequency Components ◽  
Image Contour ◽  
Objective Indicators

Background:: Aiming at the problems of color distortion, low clarity and poor visibility of underwater image caused by complex underwater environment, a wavelet fusion method UIPWF for underwater image enhancement is proposed. Methods:: First of all, an improved NCB color balance method is designed to identify and cut the abnormal pixels, and balance the color of R, G and B channels by affine transformation. Then, the color correction map is converted to CIELab color space, and the L component is equalized with contrast limited adaptive histogram to obtain the brightness enhancement map. Finally, different fusion rules are designed for low-frequency and high-frequency components, the pixel level wavelet fusion of color balance image and brightness enhancement image is realized to improve the edge detail contrast on the basis of protecting the underwater image contour. Results:: The experiments demonstrate that compared with the existing underwater image processing methods, UIPWF is highly effective in the underwater image enhancement task, improves the objective indicators greatly, and produces visually pleasing enhancement images with clear edges and reasonable color information. Conclusion:: The UIPWF method can effectively mitigate the color distortion, improve the clarity and contrast, which is applicable for underwater image enhancement in different environments.

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.

Real-Time Vehicle License Plate Recognition Based on Scanning and 2D Haar Discrete Wavelet Transform

2013 ◽  
Vol 284-287 ◽  
pp. 2402-2406 ◽  
Author(s):  
Rong Choi Lee ◽  
King Chu Hung ◽  
Huan Sheng Wang
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Real Time ◽  
Recognition Rate ◽  
Low Frequency ◽  
Experimental Result ◽  
Discrete Wavelet ◽  
License Plate ◽  
License Plate Recognition ◽  
Image Pixels

This thesis is to approach license-plate recognition using 2D Haar Discrete Wavelet Transform (HDWT) and artificial neural network. This thesis consists of three main parts. The first part is to locate and extract the license-plate. The second part is to train the license-plate. The third part is to real time scan recognition. We select only after the second 2D Haar Discrete Wavelet Transform the image of low-frequency part, image pixels into one-sixteen, thus, reducing the image pixels and can increase rapid implementation of recognition and the computer memory. This method is to scan for car license plate recognition, without make recognition of the individual characters. The experimental result can be high recognition rate.

scholarly journals Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 938
Author(s):  
Hyunho Choi ◽  
Jechang Jeong
Keyword(s):  
Additive Noise ◽  
Low Frequency ◽  
Speckle Noise ◽  
Image Filtering ◽  
Noise Removal ◽  
Discrete Wavelet ◽  
Ultrasound Images ◽  
Noise Elimination ◽  
Residual Noise ◽  
Guided Image Filtering

Ultrasound (US) imaging can examine human bodies of various ages; however, in the process of obtaining a US image, speckle noise is generated. The speckle noise inhibits physicians from accurately examining lesions; thus, a speckle noise removal method is essential technology. To enhance speckle noise elimination, we propose a novel algorithm using the characteristics of speckle noise and filtering methods based on speckle reducing anisotropic diffusion (SRAD) filtering, discrete wavelet transform (DWT) using symmetry characteristics, weighted guided image filtering (WGIF), and gradient domain guided image filtering (GDGIF). The SRAD filter is exploited as a preprocessing filter because it can be directly applied to a medical US image containing speckle noise without a log-compression. The wavelet domain has the advantage of suppressing the additive noise. Therefore, a homomorphic transformation is utilized to convert the multiplicative noise into additive noise. After two-level DWT decomposition is applied, to suppress the residual noise of an SRAD filtered image, GDGIF and WGIF are exploited to reduce noise from seven high-frequency sub-band images and one low-frequency sub-band image, respectively. Finally, a noise-free image is attained through inverse DWT and an exponential transform. The proposed algorithm exhibits excellent speckle noise elimination and edge conservation as compared with conventional denoising methods.

scholarly journals Defect Estimation in Non-Destructive Testing of Composites by Ultrasonic Guided Waves and Image Processing

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 315 ◽  
Author(s):  
Kumar Anubhav Tiwari ◽  
Renaldas Raisutis ◽  
Olgirdas Tumsys ◽  
Armantas Ostreika ◽  
Kestutis Jankauskas ◽  
...  
Keyword(s):  
Image Processing ◽  
Composite Structures ◽  
Guided Waves ◽  
Low Frequency ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Discrete Wavelet ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Non Destructive

The estimation of the size and location of defects in multi-layered composite structures by ultrasonic non-destructive testing using guided waves has attracted the attention of researchers for the last few decades. Although extensive signal processing techniques are available, there are only a few studies available based on image processing of the ultrasonic B-scan image to extract the size and location of defects via the process of ultrasonic non-destructive testing. This work presents an image processing technique for ultrasonic B-scan images to improve the estimation of the location and size of disbond-type defects in glass fiber-reinforced plastic materials with 25-mm and 51-mm diameters. The sample is a segment of a wind turbine blade with a variable thickness ranging from 3 to 24 mm. The experiment is performed by using a low-frequency ultrasonic system and a pair of contact-type piezoceramic transducers kept apart by a 50-mm distance and embedded on a moving mechanical panel. The B-scan image acquired by the ultrasonic pitch-catch technique is denoised by utilizing features of two-dimensional discrete wavelet transform. Thereafter, the normalized pixel densities are compared along the scanned distance on the region of interest of the image, and a −3 dB threshold is applied to the locations and sizes the defects in the spatial domain.

Efficient and Adaptive Rotation Invariant Wavelet Transform

2003 ◽  
Vol 01 (03) ◽  
pp. 353-372
Author(s):  
Chi-Man Pun
Keyword(s):  
Wavelet Transform ◽  
Texture Classification ◽  
Wavelet Packet ◽  
Sampling Rate ◽  
Discrete Wavelet ◽  
Wavelet Coefficients ◽  
Information Cost ◽  
Image Size ◽  
Rotation Invariant ◽  
Adaptive Wavelet

It is well known that the sensitivity to translations and orientations is a major drawback in 2D discrete wavelet transform (DWT). In this paper, we have proposed an effective scheme for rotation invariant adaptive wavelet packet transform. During decomposition, the wavelet coefficients are obtained by applying a polar transform (PT) followed by a row-shift invariant wavelet packet decomposition (RSIWPD). In the first stage, the polar transform generates a row-shifted image and is adaptive to the image size to achieve complete and minimum sampling rate. In the second stage, the RSIWPD is applied to the row-shifted image to generate rotation invariant but over completed subbands of wavelet coefficients. In order to reduce the redundancy and computational complexity, we adaptively select some subbands to decompose and form a best basis representation with minimal information cost with respect to an appropriate information cost function. With this best basis representation, the original image can be reconstructed easily by applying a row-shift invariant wavelet packet reconstruction (RSIWPR) followed by an inverse polar transform (IPT). In the experiments, we study the application of this representation for texture classification and achieve 96.5% classification accuracy.

The Research Based on Fast Motion Estimation of H.264

2012 ◽  
Vol 562-564 ◽  
pp. 2153-2156
Author(s):  
Sheng Bing Che ◽  
Han Gao
Keyword(s):  
Reference Frames ◽  
Low Frequency ◽  
Prediction Algorithm ◽  
Discrete Wavelet ◽  
Motion Vectors ◽  
Fast Motion Estimation ◽  
Macro Block ◽  
Model Segmentation ◽  
Current Block ◽  
Fast Motion

A optimization algorithm is put forward based on the brightness and frequency domain characteristics of macro block, including the algorithm of model segmentation and the prediction algorithm of starting motion vectors(MV). The basic idea of the mode segmentation is that to the unit of 16×16 block, discrete wavelet transform to frames, the after low frequency coefficients as the research object, define the coefficients sum ∑. If ∑ is greater or equal to threshold, the corresponding areas will be divided into texture areas, or smooth areas. The current block is compared with the corresponding block in reference frames. If the result is equal, the current block will be marked to background block, or sport block, and the corresponding segmentation mode is selected at the same time. The basic idea of prediction starting motion vectors algorithm is that base on the marked sport blocks, the starting MV of current block is the median value of MV to adjacent sport blocks in initial MV prediction. The experimental results show that the efficiency of algorithm in this paper has been improved.

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