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Full-Text Articles in Physical Sciences and Mathematics
Fixed-Point Proximity Minimization: A Theoretical Review And Numerical Study, Daniel Weddle, Jianfeng Guo
Fixed-Point Proximity Minimization: A Theoretical Review And Numerical Study, Daniel Weddle, Jianfeng Guo
OUR Journal: ODU Undergraduate Research Journal
This study examines the relatively recent development of a “fixed-point proximity” approach to one type of minimization problem, considers its application to image denoising, and explores convergence and divergence of the iterative algorithm beyond a (previously supplied) theoretically guaranteed convergence bound on one of the parameters (𝜆). While reviewing the fixed-point proximity approach and its application to image denoising, we aim to communicate the concepts and details in a way that will facilitate understanding for undergraduates and for scholars from other subfields. In the latter portion of our study, the numerical experiment provides thought-provoking data on the effects that parameters …
Image Denoising Using Deep Convolutional Autoencoder With Feature Pyramids, Ekrem Çeti̇nkaya, Mustafa Furkan Kiraç
Image Denoising Using Deep Convolutional Autoencoder With Feature Pyramids, Ekrem Çeti̇nkaya, Mustafa Furkan Kiraç
Turkish Journal of Electrical Engineering and Computer Sciences
Image denoising is 1 of the fundamental problems in the image processing field since it is the preliminary step for many computer vision applications. Various approaches have been used for image denoising throughout the years from spatial filtering to model-based approaches. Having outperformed all traditional methods, neural-network-based discriminative methods have gained popularity in recent years. However, most of these methods still struggle to achieve flexibility against various noise levels and types. In this paper, a deep convolutional autoencoder combined with a variant of feature pyramid network is proposed for image denoising. Simulated data generated by Blender software along with corrupted …
Region Characteristics-Based Fusion Of Spatial And Transform Domain Image Denoising Methods, Rajiv Verma, Rajoo Pandey
Region Characteristics-Based Fusion Of Spatial And Transform Domain Image Denoising Methods, Rajiv Verma, Rajoo Pandey
Turkish Journal of Electrical Engineering and Computer Sciences
Nonlocal means (NLM)- and wavelet-based image denoising methods have drawn much attention in image processing due to their effectiveness and simplicity. The performance of these algorithms varies according to region characteristics in an image. For example, NLM performs well for smooth regions due to deployment of redundancy available in images, whereas wavelet-based approaches may preserve key image features by controlling the degree of threshold for shrinking the noisy coefficients. This paper presents a simple novel approach that estimates an original image by simply taking the weighted average of the denoised images pixel values obtained by NLM and wavelet thresholding schemes …
An Algorithm For Image Restoration With Mixed Noise Using Total Variation Regularization, Cong Thang Pham, Guilhem Gamard, Andrei Kopylov, Thi Thu Thao Tran
An Algorithm For Image Restoration With Mixed Noise Using Total Variation Regularization, Cong Thang Pham, Guilhem Gamard, Andrei Kopylov, Thi Thu Thao Tran
Turkish Journal of Electrical Engineering and Computer Sciences
We present here an effective scheme for image denoising based on total variation regularization. The proposed scheme allows to efficiently remove Poisson noise as well as Gaussian noise simultaneously with the help of a new kind of data fidelity term, suitable for the mixed Poisson - Gaussian noise model. The results show that the algorithm corresponding to our new scheme outperforms the existing methods for mixed Poisson - Gaussian noise removal.
A New Method Based On Pixel Density In Salt And Pepper Noise Removal, Uğur Erkan, Levent Gökrem
A New Method Based On Pixel Density In Salt And Pepper Noise Removal, Uğur Erkan, Levent Gökrem
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, we deliver a new method to remove salt and pepper noise, which we refer to as based on pixel density filter (BPDF). The first step of the method is to determine whether or not a pixel is noisy, and then we decide on an adaptive window size that accepts the noisy pixel as the center. The most repetitive noiseless pixel value within the window is set as the new pixel value. By using 18 test images, we give the results of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), image enhancement factor (IEF), standard median filter (SMF), adaptive …
Removing Random-Valued Impulse Noise In Images Using A Neural Network Detector, İlke Türkmen
Removing Random-Valued Impulse Noise In Images Using A Neural Network Detector, İlke Türkmen
Turkish Journal of Electrical Engineering and Computer Sciences
This paper proposes a new method using an artificial neural network to remove random-valued impulse noise (RVIN) in images. The inputs of the neural model used to detect the RVIN are formed using basic and related gradient values. The detection of the noisy pixels is realized in 3 phases using the proposed neural detector. In order to obtain a more robust detector, 2 different networks, which are trained with an artificial training image corrupted with high and low clutter densities, are used. The extensive simulation results show that the proposed method is significantly better than the compared filters in terms …