Image Denoising Using Various Image Enhancement Techniques

2020 ◽  
pp. 179-191
Author(s):  
S. P. Premnath ◽  
J. Arokia Renjith
Keyword(s):  
Image Enhancement ◽  
Image Denoising

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.

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 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.

An Algorithm for Image Enhancement via Sparse Representation

2014 ◽  
Vol 556-562 ◽  
pp. 4806-4810 ◽  
Author(s):  
Li Liang Zhang ◽  
Xi Ling Liu ◽  
Shi Liang Zhang
Keyword(s):  
Sparse Representation ◽  
Image Enhancement ◽  
Image Denoising ◽  
Texture Component ◽  
Visual Quality ◽  
Morphological Component Analysis ◽  
Morphological Component ◽  
Different Content ◽  
Popular Image ◽  
Denoised Image

This paper presents an approach of enhance images subjective visual quality, based on image sparse representation. Firstly, comparativing and analysing the performance of the current several popular image denoising methods by two kinds of different content image, and using the K-SVD, MB3D and CSR algorithm, we obtain clean images namely the images noise removing. Then, decomposing the already denoised image into both cartoon and texture component by Morphological Component Analysis (MCA ) method, and superresolution the cartoon part and enhance the contrast of the texture in image. Finally, fusion between the cartoon and the texture gain the desired image.

Exploring Coronal Structures with SOHO

2000 ◽  
Vol 179 ◽  
pp. 403-406
Author(s):  
M. Karovska ◽  
B. Wood ◽  
J. Chen ◽  
J. Cook ◽  
R. Howard
Keyword(s):  
Solar Corona ◽  
Image Enhancement ◽  
Coronal Mass Ejections ◽  
Dynamical Evolution ◽  
Small Scale ◽  
Low Contrast ◽  
Contrast Structures ◽  
Scale Structures ◽  
Small Scale Structures ◽  
Coronal Structures

AbstractWe applied advanced image enhancement techniques to explore in detail the characteristics of the small-scale structures and/or the low contrast structures in several Coronal Mass Ejections (CMEs) observed by SOHO. We highlight here the results from our studies of the morphology and dynamical evolution of CME structures in the solar corona using two instruments on board SOHO: LASCO and EIT.

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