scholarly journals Three robust edges stopping functions for image denoising

2022 ◽  
Vol 40 ◽  
pp. 1-12
Author(s):  
Hicham Rezgui ◽  
Messaoud Maouni ◽  
Mohammed Lakhdar Hadji ◽  
Ghassen Touil
Keyword(s):  
Image Enhancement ◽  
Image Denoising ◽  
Image Noise

In this paper, we present three strong edge stopping functions for image enhancement. These edge stopping functions have the advantage of effectively removing the image noise while preserving the true edges and other important features. The obtained results show an improved quality for the restored images compared to existing restoration models.

scholarly journals 2D–1D Wavelet Reconstruction as a Tool for Source Finding in Spectroscopic Imaging Surveys

2012 ◽  
Vol 29 (3) ◽  
pp. 244-250 ◽  
Author(s):  
L. Flöer ◽  
B. Winkel
Keyword(s):  
Image Enhancement ◽  
Image Denoising ◽  
Spectroscopic Data ◽  
Wavelet Decomposition ◽  
Spectroscopic Imaging ◽  
Basic Method ◽  
Spectral Dimension ◽  
Wavelet Coefficients ◽  
Data Cubes ◽  
Spatial Dimensions

AbstractToday, image denoising by thresholding of wavelet coefficients is a commonly used tool for 2D image enhancement. Since the data product of spectroscopic imaging surveys has two spatial dimensions and one spectral dimension, the techniques for denoising have to be adapted to this change in dimensionality. In this paper we will review the basic method of denoising data by thresholding wavelet coefficients and implement a 2D–1D wavelet decomposition to obtain an efficient way of denoising spectroscopic data cubes. We conduct different simulations to evaluate the usefulness of the algorithm as part of a source finding pipeline.

Non-Subsampled Contourlet Transform-Based Effective Denoising of Medical Images

2019 ◽  
pp. 155-183
Author(s):  
Karthikeyan P. ◽  
Vasuki S. ◽  
Karthik K.
Keyword(s):  
Image Analysis ◽  
Edge Detection ◽  
Image Enhancement ◽  
Image Denoising ◽  
Medical Images ◽  
Noise Removal ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Shift Invariance ◽  
Image Edges

Noise removal in medical images remains a challenge for the researchers because noise removal introduces artifacts and blurring of the image. Developing medical image denoising algorithm is a difficult operation because a tradeoff between noise reduction and the preservation of actual features of image has to be made in a way that enhances and preserves the diagnostically relevant image content. A special member of the emerging family of multiscale geometric transforms is the contourlet transform which effectively captures the image edges and contours. This overcomes the limitations of the existing method of denoising using wavelet and curvelet. But due to down sampling and up sampling, the contourlet transform is shift-variant. However, shift-invariance is desirable in image analysis applications such as edge detection, contour characterization, and image enhancement. In this chapter, nonsubsampled contourlet transform (shift-invariance transform)-based denoising is presented which more effectively represents edges than contourlet transform.

scholarly journals Image enhancement in wavelet domain based on histogram equalization and median filter

2021 ◽  
Author(s):  
Haval Sulaiman Abdullah ◽  
◽  
Firas Mahmood Mustafa ◽  
Atilla Elci ◽  
◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Noise Reduction ◽  
Discrete Wavelet Transform ◽  
Image Enhancement ◽  
Median Filter ◽  
Histogram Equalization ◽  
Image Noise ◽  
Discrete Wavelet ◽  
Wavelet Domain ◽  
Image Noise Reduction

During the acquisition of a new digital image, noise may be introduced as a result of the production process. Image enhancement is used to alleviate problems caused by noise. In this work, the purpose is to propose, apply, and evaluate enhancement approaches to images by selecting suitable filters to produce improved quality and performance results. The new method proposed for image noise reduction as an enhancement process employs threshold and histogram equalization implemented in the wavelet domain. Different types of wavelet filters were tested to obtain the best results for the image noise reduction process. Also, the effect of canceling one or more of the high-frequency bands in the wavelet domain was tested. The mean square error and peak signal to noise ratio are used for measuring the improvement in image noise reduction. A comparison made with two related works shows the superiority of the methods proposed and implemented in this research. The proposed methods of applying the median filter before and after the histogram equalization methods produce improvement in performance and efficiency compared to the case of using discrete wavelet transform only, even with the cases of multiresolution discrete wavelet transform and the cancellation step.

Adaptive Filter with Type-2 Fuzzy System and Optimization-Based Kernel Interpolation for Satellite Image Denoising

2020 ◽  
Vol 63 (6) ◽  
pp. 913-926
Author(s):  
T Mahalakshmi ◽  
Alluri Sreenivas
Keyword(s):  
Image Enhancement ◽  
Image Denoising ◽  
Adaptive Filter ◽  
Optimization Algorithm ◽  
Fuzzy System ◽  
Satellite Images ◽  
Satellite Image ◽  
Similarity Index ◽  
Noise Correction

Abstract Satellite image denoising is a recent trend in image processing, but faces many challenges due to the environmental factors. Previous works have developed many filters for denoising the hyperspectral satellite images. Accordingly, this work utilizes an adaptive filter with the type 2 fuzzy system and the optimization-based kernel interpolation for the satellite image denoising. Here, the image denoising has been done through three steps, namely noise identification, noise correction and image enhancement. Initially, the type 2 fuzzy system identifies the noisy pixels in the satellite image and converts the image into a binary image, which is passed through the adaptive nonlocal mean filter (ANLMF) for the noise correction. Finally, the kernel-based interpolation scheme carries out the image enhancement, which is done through the proposed chronological Jaya optimization algorithm (chronological JOA) that is developed by modifying Jaya optimization algorithm (JOA) with the chronological idea. The performance of the proposed denoising scheme is analyzed by considering the satellite images from two standard databases, namely Indian pines database and NRSC/ISRO satellite database. Also, the comparative analysis is performed with the state-of-the-art denoising methods using the evaluation metrics, peak signal to noise ratio (PSNR), structural similarity index (SSIM) and second derivative-like measure of enhancement (SDME). From the results, it is exposed that the proposed adaptive filter with the chronological JOA has the improved performance with the PSNR of 22.0408 dB, SDME of 244.133 dB and SSIM of 0.872.

scholarly journals Adaptive Total Variation Minimization-Based Image Enhancement from Flash and No-Flash Pairs

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Sang Min Yoon ◽  
Yeon Ju Lee ◽  
Gang-Joon Yoon ◽  
Jungho Yoon
Keyword(s):  
Image Enhancement ◽  
Image Denoising ◽  
Total Variation ◽  
Main Idea ◽  
Numerical Results ◽  
Ambient Light ◽  
Total Variation Minimization ◽  
Novel Approach ◽  
Adaptive Total Variation

We present a novel approach for enhancing the quality of an image captured from a pair of flash and no-flash images. The main idea for image enhancement is to generate a new image by combining the ambient light of the no-flash image and the details of the flash image. In this approach, we propose a method based on Adaptive Total Variation Minimization (ATVM) so that it has an efficient image denoising effect by preserving strong gradients of the flash image. Some numerical results are presented to demonstrate the effectiveness of the proposed scheme.

Color image enhancement based on LLL tricolor image denoising and fusion

2018 ◽  
Vol 39 (1) ◽  
pp. 52-57 ◽  
Author(s):  
Wu Haibing ◽  
Zhang Liang ◽  
Gu Guohua ◽  
Tao Haijun ◽  
Ning Quanli
Keyword(s):  
Image Enhancement ◽  
Image Denoising ◽  
Color Image ◽  
Color Image Enhancement

scholarly journals Image Denoising With Generative Adversarial Networks and its Application to Cell Image Enhancement

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 82819-82831
Author(s):  
Songkui Chen ◽  
Daming Shi ◽  
Muhammad Sadiq ◽  
Xiaochun Cheng
Keyword(s):  
Image Enhancement ◽  
Image Denoising ◽  
Generative Adversarial Networks ◽  
Cell Image ◽  
Adversarial Networks

scholarly journals Facial image noise classification and denoising using neural network

2021 ◽  
Vol 3 (2) ◽  
pp. 102-111
Author(s):  
Milan Tripathi
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Image Denoising ◽  
Image Noise ◽  
Facial Image ◽  
Noisy Images ◽  
Image Dataset ◽  
Noise Classification

Image denoising is an important aspect of image processing. Noisy images are produced as a result of technical and environmental flaws. As a result, it is reasonable to consider image denoising an important topic to research, as it also aids in the resolution of other image processing issues. The challenge, however, is that the traditional techniques used are time-consuming and inflexible. This article purposed a system of classifying and denoising noised images. A CNN and UNET based model architecture is designed, implement, and evaluated. The facial image dataset is processed and then it is used to train, valid and test the models. During preprocessing, the images are resized into 48*48, normalize, and various noises are added to the image. The preprocessing for each model is a bit different. The training and validation accuracy for the CNN model is 99.87% and 99.92% respectively. The UNET model is also able to get optimal PSNR and SSIM values for different noises.

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.

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