An adaptive bitonic filtering based edge fusion algorithm for Gaussian denoising

Goyal, Bhawna; Gupta, Anupma; Dogra, Ayush; Koundal, Deepika

doi:10.1016/j.ijcce.2022.03.001

Cited by 11 publications

(8 citation statements)

References 24 publications

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“…The wavelet thresholding and spatial domain filtering are implemented in the Bayesian domain. This technique is introduced to reduce noise from the input images [16]. Method noise is the variation of the denoised image attained from a specific denoising algorithm and the original image.…”

Section: Pre-processing: Gaussian/bilateral Filter Methods Noise Thre...mentioning

confidence: 99%

Automatic Detection of Covid-19 in CT Images Using an Optimized Deep Neural Network Classifier

G. Prabakaran, Dr. K. Jayanthi

2023

tjjpt

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An exact and quick finding of Covid-19 patients plays a significant part in the initial period of medicinal treatment and prevention. Automatic recognition of COVID-19 cases using CT images may help lessen the effect of this infection on human civilization. In recent years, computer vision is the major solution for diagnosing the covid-19 disease by using CT images. Besides, many researchers had presented efficient artificial intelligence techniques for identifying Covid-19 disease. Nevertheless, the accuracy and time consumption of the model further to be improved. Thus, in this work, we propose a Covid-19 disease classification using a hybrid deep neural network with handcrafted features. The proposed approach consists of three stages namely, pre-processing, handcrafted feature extraction, and classification. Initially, the images are given to the pre-processing stage to remove the noise present in the input images. Then, we extract the hand-crafted features (Gray-Level Co-Occurrence Matrix (GLCM)) from each image. After that, the pre-processed image is given to the input of the optimized deep neural network classifier to classify an image as normal or abnormal. The proposed optimized deep neural network is a combination of a convolution neural network (CNN) and adaptive artificial jelly optimization (A2JO) algorithm. To enhance the performance of classifier accuracy, the extracted handcrafted features are fused with the hybrid deep neural network. The performance of the proposed approach was analyzed based on different metrics and performance compared with other techniques.

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Section: Pre-processing: Gaussian/bilateral Filter Methods Noise Thre...mentioning

confidence: 99%

Automatic Detection of Covid-19 in CT Images Using an Optimized Deep Neural Network Classifier

G. Prabakaran, Dr. K. Jayanthi

2023

tjjpt

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“…Similarly, Ahamed et al 26 utilized various filters, such as Weiner filter, median filter, average filter, disk filter, and Gaussian filter to remove the salt-and-pepper noise from the images. Similarly, Goyal et al 27 removed the Gaussian noise using the dual-way edge fusion scheme. First, noisy and bitonic-filtered images were subtracted to obtain the resultant data.…”

Section: Traditional-based Approachesmentioning

confidence: 99%

Deep stacked denoising autoencoder for unsupervised anomaly detection in video surveillance

Roka

Diwakar

2023

J. Electron. Imag.

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.Due to the increase of crime and terror, security concerns are rising rapidly every day. The use of surveillance cameras for abnormal behavior detection has become an indispensable part of human beings. But the performance of most of the developed systems is not up to the mark because of the low performance and accuracy in detecting the abnormality in the videos due to mainly the presence of noise. The videos captured by the surveillance camera are generally born with no or more noise due to various reasons. To resolve such issues, we provide a snapshot regarding different categories of noise and handcraft techniques to resolve them. Non-local means, block matching, and 3D filtering filters perform astonishingly well while denoising the images. We also present a robust unsupervised deep learning model called deep stacked denoising autoencoder (DSDAE) for denoising the images and further use it for abnormal activity detection and localization in the videos. Our approach has achieved a noteworthy result in image denoising compared to other handcraft-based techniques. DSDAE uses a separate encoder for the extraction of appearance features using clean and noisy images and motion features through the optical flow images. Early fusion is done in the extracted features and passed to the decoder. Only those pixels whose reconstruction error is greater than the threshold will be considered abnormal pixels. Experiment results are compared quantitatively/qualitatively with the recent competitive state-of-the-art methods in the publicly available benchmark datasets Ped1, Ped2, CUHK Avenue, and ShanghaiTech that demonstrate the superior accuracy and performance of our DSDAE. The obtained area under the curve of DSDAE in Ped1, Ped2, CUHK Avenue, and ShanghaiTech is 98.14%, 97.92%, 95.89%, and 96.7%, respectively, whereas equal error rate for the same datasets is 5.4%, 4.5%, 12.03%, and 7.8%, respectively.

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“…Therefore, image quality assessment (IQA) has found many applications and become a hot research topic in the research community [ 1 ]. Namely, IQA methods evaluate the perceptual quality of digital images and support, among others, image enhancement [ 2 ], restoration [ 3 ], steganography [ 4 ], or denoising algorithms [ 5 ]. Further, IQA is also necessary in the benchmarking of many image processing or computer-vision algorithms [ 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Full-Reference Image Quality Assessment Based on an Optimal Linear Combination of Quality Measures Selected by Simulated Annealing

Varga

2022

J. Imaging

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Digital images can be distorted or contaminated by noise in various steps of image acquisition, transmission, and storage. Thus, the research of such algorithms, which can evaluate the perceptual quality of digital images consistent with human quality judgement, is a hot topic in the literature. In this study, an image quality assessment (IQA) method is introduced that predicts the perceptual quality of a digital image by optimally combining several IQA metrics. To be more specific, an optimization problem is defined first using the weighted sum of a few IQA metrics. Subsequently, the optimal values of the weights are determined by minimizing the root mean square error between the predicted and ground-truth scores using the simulated annealing algorithm. The resulted optimization-based IQA metrics were assessed and compared to other state-of-the-art methods on four large, widely applied benchmark IQA databases. The numerical results empirically corroborate that the proposed approach is able to surpass other competing IQA methods.

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An adaptive bitonic filtering based edge fusion algorithm for Gaussian denoising

Cited by 11 publications

References 24 publications

Automatic Detection of Covid-19 in CT Images Using an Optimized Deep Neural Network Classifier

Automatic Detection of Covid-19 in CT Images Using an Optimized Deep Neural Network Classifier

Deep stacked denoising autoencoder for unsupervised anomaly detection in video surveillance

Full-Reference Image Quality Assessment Based on an Optimal Linear Combination of Quality Measures Selected by Simulated Annealing

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