Evaluation of image denoising techniques a performance perspective

Kumar, G. Arvind; Kusagur, Ashok

doi:10.1109/scopes.2016.7955762

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…With this approach, authors distinguished two categories of image enhancement and concluded that the frequency domain approach presented a much easier implementation compared to the spatial domain approach. Kumar and Kusagur [37] examined a MATLAB-based performance evaluation of various image denoising techniques on several images. The aim of the evaluation was to further highlight recent improvements in MATLAB-based image denoising techniques compared to the conventional noise filtering approaches.…”

Section: Review Of Related Workmentioning

confidence: 99%

Image processing system using MATLAB-based analytics

et al. 2021

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Owing to recent technological advancement, computers and other devices running several image editing applications can be further exploited for digital image processing operations. This paper evaluates various image processing techniques using matrix laboratory (MATLAB-based analytics). Compared to the conventional techniques, MATLAB gives several advantages for image processing. MATLAB-based technique provides easy debugging with extensive data analysis and visualization, easy implementation and algorithmic-testing without recompilation. Besides, MATLAB's computational codes can be enhanced and exploited to process and create simulations of both still and video images. Moreover, MATLAB codes are much concise compared to C++, thus making it easier for perusing and troubleshooting. MATLAB can handle errors prior to execution by proposing various ways to make the codes faster. The proposed technique enables advanced image processing operations such as image cropping/resizing, image denoising, blur removal, and image sharpening. The study aims at providing readers with the most recent MATLAB-based image processing application-tools. We also provide an empirical-based method using two-dimensional discrete cosine transform (2D-DCT) derived from its coefficients. Using the most recent algorithms running on MATLAB toolbox, we performed simulations to evaluate the performance of our proposed technique. The results largely present MATLAB as a veritable approach for image processing operations.

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Section: Review Of Related Workmentioning

confidence: 99%

Image processing system using MATLAB-based analytics

et al. 2021

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“…图像去噪和重构作为图像处理的经典问题一直受到关注, 目前去噪方法总体上可以分为非局部均值法, 各项异性扩散去噪, 双边滤波去噪和统计模型去噪方法等 [24,25] . 尽管随着实际应用的需求, 去噪方法不断推陈出新, 比如在遥感图像去噪和重建领域出现了一些新的去噪方法 [26∼29] , 但目前对研究者而言如何在有效去除噪声的同时很好地保留图像的特征及纹理信息仍是一个具有挑战性的问题 [30] . 本文所提出的 M-NSST-HMT 模型很好的揭示了图像 NSST 子带系数间的关系, 并将该模型应用于图像去噪, 为后续的图像处理奠定了基础.…”

Section: 所提出 Htm 模型在图像去噪中的应用unclassified

Image NSST-HMT model with associated multi-state coefficients

et al. 2019

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In recent years, due to its anisotropy, multi-directional capture characteristics, and translation invariance, the non-subsampled shearlet transform (NSST) has played an important stabilizing role in the process of image restoration. In this study, we analyze an image's NSST coefficients, including the relationship between coefficients in the same subband, the relationship between "father-son" coefficients, and the relationship between "brotherhood" coefficients in different subbands. The results reveal that the coefficients in the NSST subbands are sparse, and both "father-son relationship" and "brotherhood relationship" coefficients exhibit aggregation and transitivity. On this basis, a hidden Markov tree (HMT) model with associated multi-state coefficients (M-NSST-HMT) is proposed. This model estimates the reconstructed coefficients using "father-son relationship" and "brotherhood relationship" of the NSST subband coefficients as joint states of guiding the coefficients' transfer between subbands. In addition, the model integrates the reconstructed coefficients using the mutual information between these two associated states. Finally, the proposed model is applied to image denoising with favorable results. The results indicate that the proposed model can reveal the relationship of coefficients in NSST subbands and improve the prediction accuracy of coefficients more effectively than the traditional HMT model.

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“…(12) = + × 100 (13) In addition, accuracy is another suitable evaluation metric for finding the effectiveness of the noise detection system. The general formula of accuracy for determining the noise detection is given in the Eq.…”

Section: = + × 100mentioning

confidence: 99%

“…Most of the existing methods cannot judge whether the corrupted image is affected by which type of noise in advance [9], [10]. So, it is significant to construct a detector in digital images before performing denoising algorithms like discrete cosine transform [11], digital image correlation [12], decision based filtering [13], etc. In order to deal with the above problems, this paper presented a method to detect and remove the noises (Gaussian and impulse) from the digital images.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Noise Detection Using Texture Feature Extraction and Random Forest Classification

Ganesh¹,

Kusagur²

2018

IJIES

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Image noise detection is one of the most challenging aspect in the field of image processing and analysis. Generally, the noise affects the information of image, especially when the noise level is too high. To avoid this kind of issue, image denoising schemes is developed for several applications: medical image analysis, pattern recognition, biometric authentication, etc. to reduce the noise level in the image. This experimental research concentrates on two objectives i.e. identify the noise in the corrupted image and suggest the denoising algorithm for the respective noise. To perform this operation, a new methodology is developed, initially, noise subtraction is carried-out to separate the noisy image and higher frequency image components from the digital images. Then, feature extraction is carried-out on noisy image using texture features like homogeneity, contrast, correlation, energy and two Dimension-Discrete Wavelet Transform (DWT) for extracting the optimal feature values. The obtained feature vectors are given as the input for multi-objective classifier: Random Forest (RF) in order to classify the type of noise. Experimental outcome showed that the proposed technique improved the noise prediction accuracy up to 5-20% compared to the existing methods: Neural Network (NN), Naïve Bayes (NB) classifier, K-Nearest Neighbor (KNN) and Convolutional Neural Network (CNN) by means of accuracy, sensitivity and specificity.

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Evaluation of image denoising techniques a performance perspective

Cited by 6 publications

References 4 publications

Image processing system using MATLAB-based analytics

Image processing system using MATLAB-based analytics

Image NSST-HMT model with associated multi-state coefficients

Adaptive Noise Detection Using Texture Feature Extraction and Random Forest Classification

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