scholarly journals IMAGE DENOISING: A COMPARATIVE STUDY OF VARIOUS WAVELET APPROACHES

2021 ◽  
Vol 9 (1) ◽  
pp. 1045-1060
Author(s):  
Laavanya Mohan, Vijayaragahvan Veeramani
Keyword(s):  
Image Denoising ◽  
Wavelet Transforms ◽  
Hybrid Methods ◽  
Wavelet Coefficient ◽  
Threshold Value ◽  
Great Success ◽  
Research Article ◽  
Degraded Image ◽  
Coefficient Shrinkage ◽  
Threshold Approach

Image denoising is a major tricky in image processing. The main determination is to quash noise from the degraded image while keeping other details of the image unchanged. In recent years, many multi-resolution based approaches have attained great success in image denoising. In a nut shell, the wavelet transform provide an optimal representation of a noisy image, including a signal with information from a limited number of coefficients and noise by all other left over coefficients. The most popular way to eliminate noise is to threshold the noise affected wavelet coefficient. The noise affected wavelet coefficient shrinkage is better, only if the threshold value is properly selected. Therefore, the performance of various wavelet based denoising techniques depends on the estimation of the threshold value. Different techniques are available to find the threshold value. The aim of this study is to discuss denoising schemes based on various wavelet transforms using threshold approach. Hence, this article examines the research article with threshold selection based on spatial adaptivity, sub-band adaptivity and also hybrid methods with multi-resolution wavelet structures.

Characterization of local regions for wavelet-based image denoising using a statistical approach

2020 ◽  
Vol 18 (03) ◽  
pp. 2050011
Author(s):  
Rajiv Verma ◽  
Rajoo Pandey
Keyword(s):  
Image Denoising ◽  
Statistical Approach ◽  
Wavelet Coefficient ◽  
Threshold Value ◽  
Vital Role ◽  
Wiener Filtering ◽  
Original Signal ◽  
Wavelet Coefficients ◽  
Local Window

The shape of local window plays a vital role in the estimation of original signal variance, which is used to shrink the noisy wavelet coefficients in wavelet-based image denoising algorithms. This paper presents an anisotropic-shaped region-based Wiener filtering (ASRWF) and BayesShrink (ASRBS) algorithms, which exploit the region characteristics to estimate the original signal variance using a statistical approach. The proposed approach divides the region centered on a noisy wavelet coefficient into various non-overlapping subregions. The Euclidean distance-based measure is considered to obtain the similarities between reference subregion and adjacent subregions. An appropriate threshold value is estimated by applying a statistical approach on these distances and the sets of similar and dissimilar subregions are obtained from a defined region. Thus, an anisotropic-shaped region is obtained by neglecting the dissimilar subregions in a defined region. The variance of every similar subregion is calculated and then averaged to estimate the original signal variance to shrink noisy wavelet coefficients effectively. Finally, the estimated signal variance is utilized in Wiener filtering and BayesShrink algorithms to improve the denoising performance. The performance of the proposed algorithms is analyzed qualitatively and quantitatively on standard images for different noise levels.

scholarly journals Convolutional Neural Network Based Image Denoising for Better Quality of Images

2018 ◽  
Vol 7 (3.27) ◽  
pp. 356
Author(s):  
Anandbabu Gopatoti ◽  
Merajothu Chandra Naik ◽  
Kiran Kumar Gopathoti
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Denoising ◽  
Wavelet Transforms ◽  
Visual Information ◽  
Wavelet Coefficient ◽  
Image Data ◽  
Vital Role ◽  
Adaptive Wavelet ◽  
Comparative Accuracy

This work gives a survey by comparing the different methods of image denoising with the help of wavelet transforms and Convolutional Neural Network. To get the better method for Image denoising, there is distinctive merging which have been used. The vital role of communication is transmitting visual information in the appearance of digital images, but on the receiver side we will get the image with corruption. Therefore, in practical analysis and facts, the powerful image denoising approach is still a legitimate undertaking. The algorithms which are very beneficial for processing the signal like compression of image and denoising the image is Wavelet transforms. To get a better quality image as output, denoising methods includes the maneuver of data of that image. The primary aim is wavelet coefficient modification inside the new basis, by that the noise within the image data can be eliminated. In this paper, we suggested different methods of image denoising from the corrupted images with the help of different noises like Gaussian and speckle noises. This paper implemented by using adaptive wavelet threshold( Sure Shrink, Block Shrink, Neigh Shrink and  Bivariate Shrink) and Convolutional Neural Network(CNN) Model, the experimental consequences the comparative accuracy of our proposed work.  

A Method to Enhance the Fog Image Based on Dark Object Subtraction

2014 ◽  
Vol 543-547 ◽  
pp. 2484-2487
Author(s):  
Jing Zhang ◽  
Wei Dong ◽  
Jian Xin Wang ◽  
Xu Ning Liu
Keyword(s):  
Dynamic Range ◽  
Color Space ◽  
Threshold Value ◽  
Entropy Method ◽  
Image Contrast ◽  
Dark Object ◽  
Image Contrast Enhancement ◽  
Degraded Image ◽  
Enhancement Method ◽  
Rgb Color Space

Aiming at the problem of poor image contrast and low visibility, a single image contrast enhancement method is put forward in this paper. The method is based on Dark-object subtraction technique, translating the fog degraded image from RGB color space to YIQ color space, and taking out the Y component. Then using the maximum entropy method to get the threshold value of image segmentation, we can put different portion of the image according to the different formula for image restoration. The processed image must be converted from YIQ color space to RGB color space In the back of the steps. Finally, the image needs a linear dynamic range adjustment to enhance the contrast and brightness. Experiments show that the method can effectively remove haze effect on the image. The dehazing effect of the processed image is obvious. The image becomes clear and bright, and the details is outstanding, which is convenient for observation and analysis.

Signal quantitative analysis using customizable perfect-translation-invariant complex wavelet functions

2014 ◽  
Vol 12 (04) ◽  
pp. 1460010 ◽  
Author(s):  
Hiroshi Toda ◽  
Zhong Zhang
Keyword(s):  
Quantitative Analysis ◽  
Frequency Band ◽  
Wavelet Transforms ◽  
Wavelet Coefficient ◽  
Discrete Wavelet ◽  
Digital Signals ◽  
Orthonormal Wavelet ◽  
Translation Invariant ◽  
Fast Calculation ◽  
Complex Wavelet

In this paper, we introduce several methods of signal quantitative analysis using the perfect-translation-invariant complex wavelet functions (PTI complex wavelet functions), which are used in our proposed perfect-translation-invariant complex discrete wavelet transforms (PTI CDWTs) and can be designed by customization. First, using PTI complex wavelet functions, we define the continuous wavelet coefficient (CWC). Next, using orthonormal wavelet functions in the classical Hardy space, we analyze the CWC, and show that, using a CWC, we can measure the energy of a customizable frequency band, and additionally, using numbers of CWCs, we can measure the energy of the whole frequency band. Next, we introduce the fast calculation method of CWCs and show the applicability of the PTI CDWTs to digital signals. Based on them, we introduce some examples of signal quantitative analysis, including the methods to obtain instantaneous amplitude, instantaneous phase and instantaneous frequency. Additionally, we introduce the energy measurement of the whole frequency band using the PTI DT-CDWT, which is one of our proposed PTI CDWTs.

scholarly journals Autoencoder-based patch learning for real-world image denoising

2019 ◽  
Vol 13 ◽  
pp. 174830261988139
Author(s):  
Fei Chen ◽  
Haiqing Chen ◽  
Xunxun Zeng ◽  
Meiqing Wang
Keyword(s):  
Image Denoising ◽  
Real World ◽  
State Of The Art ◽  
Compact Representation ◽  
Natural Image ◽  
Great Success ◽  
Image Patch ◽  
Self Similarity ◽  
Coding Scheme ◽  
World Image

Internal patch prior (e.g. self-similarity) has achieved a great success in image denoising. However, it is a challenging task to utilize clean external natural patches for denoising. Natural image patch comes from very complex distributions which are hard to learn without supervision. In this paper, we use an autoencoder to discover and utilize these underlying distributions to learn a compact representation that is more robust to realistic noises. By exploiting learned external prior and internal self-similarity jointly, we develop an efficient patch sparse coding scheme for real-world image denoising. Numerical experiments demonstrate that the proposed method outperforms many state-of-the-art denoising methods, especially on removing realistic noise.

scholarly journals A NEW APPROACH FOR IDENTIFYING SLEEP APNEA SYNDROME USING WAVELET TRANSFORM AND NEURAL NETWORKS

2006 ◽  
Vol 18 (03) ◽  
pp. 138-143 ◽  
Author(s):  
ROBERT LIN ◽  
REN-GUEY LEE ◽  
CHWAN-LU TSENG ◽  
HENG-KUAN ZHOU ◽  
CHIH-FENG CHAO ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Sleep Apnea ◽  
Wavelet Transforms ◽  
Wavelet Coefficient ◽  
Sleep Apnea Syndrome ◽  
Eeg Signal ◽  
Spectral Components ◽  
Apnea Syndrome ◽  
Artificial Neural

This paper describes a new technique to classify and analyze the electroencephalogram (EEG) signal and recognize the EEG signal characteristics of Sleep Apnea Syndrome (SAS) by using wavelet transforms and an artificial neural network (ANN). The EEG signals are separated into Delta, Theta, Alpha, and Beta spectral components by using multi-resolution wavelet transforms. These spectral components are applied to the inputs of the artificial neural network. We treated the wavelet coefficient as the kind of the training input of artificial neural network, might result in 6 groups of wavelet coefficients per second signal by way of characteristic part processing technique of the artificial neural network designed by our group, we carried out the task of training and recognition of SAS symptoms. Then the neural network was configured to give three outputs to signify the SAS situation of the patient. The recognition threshold for all test signals turned out to have a sensitivity level of approximately 69.64% and a specificity value of approximately 44.44%. In neurology clinics, this study offers a clinical reference value for identifying SAS, and could reduce diagnosis time and improve medical service efficiency.

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