Progression approach for image denoising

Charmouti, Bilal; Junoh, Ahmad Kadri; Mashor, Mohd Yusoff; Ghazali, Najah; Wahab, Mahyun Ab; Muhamad, Wan Zuki Azman Wan; Yahya, Zainab; Beroual, Abdesselam

doi:10.12928/telkomnika.v17i6.12408

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…In this case, filtering stands out as a neighborhood operation. The value of a subject pixel (x, y) in an output image is usually determined by applying algorithms to the values of the pixels in the neighborhood of corresponding input pixels [23,24]. In a broader sense, a neighborhood pixel can be determined based on locations relative to that pixel.…”

Section: Spatial Domain Filtersmentioning

confidence: 99%

Image Denoising Based on Implementing Threshold Techniques in Multi-Resolution Wavelet Domain and Spatial Domain Filters

Ismael¹,

Baykara²

2022

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Nowadays, a digital image is often easily corrupted due to different forms of noise and complex processes resulting from the acquisition, compression, encoding, transportation, storage, retrieval, etc. All of these factors cause image quality to be distorted and visual information to be lost; in order to overcome this problem, Image denoising techniques are used widely to eliminate the various forms of noise that exist in the deteriorating image while keeping as many fine details and vital signal features as possible in the digital image. The wavelet denoising method aims to remove unwanted noise from a noisy image while preserving its vital features as a result of its ability to divide the degraded image into four sub-bands (sub-images) and operate at the frequencies of each one separately, where acquiring the original image content is vital to achieving reliable performance. This work introduces and implements a new hybrid system to the image denoising caused by Additive White Gaussian Noise (AWGN). The hybrid system is achieved using a combination of Median and Wiener filters as spatial domain filters with two-dimensional stationary and discrete wavelet transform (2D-SWT, 2D-DWT) as a multi-resolution analysis technique by applying 131 wfilters from the wavelet families (haar, db, sym, coif, bior, rbio, dmey, fk) in image processing at three levels of decomposition based on Hard, SureShrink, Bayesian, and Penalized threshold techniques on both high and low frequencies to distinguish and remove noise from affected pixel units and obtain improved results of the noise reduction process to the noisy image. Then, the multi-level 2D inverse wavelet transform (2D-IWT) eliminates noise and completes the image reconstruction by the hybrid denoising technique. Finally, the performance of the hybrid system has been estimated and measured by the peak signal-to-noise ratio (PSNR) value as an image quality metric. Experimental evaluation findings that the results of the proposed approach improved by about 17.5% by comparing them to the results of the related work, as well as enhancement the essence of image quality in terms of better noise reduction and edge preservation instead of using a multi-resolution WT domain or spatial domain filters separately.

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Section: Spatial Domain Filtersmentioning

confidence: 99%

Image Denoising Based on Implementing Threshold Techniques in Multi-Resolution Wavelet Domain and Spatial Domain Filters

Ismael¹,

Baykara²

2022

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“…In the last three decades, many algorithms [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] that hinge on distinctive techniques have been investigated and evolved for noise apprehension and noise dissolving [10][11][12][13][14][15][16][17][18][19][20] in order to restore profaned portraits because there are great requirements for high quality portraits, which have been used in modern complex applications [1][2][3][4] such as medical imaging [7][8][9], remote sensing [6], super resolution (SR) [5],…”

Section: Sivve Noisementioning

confidence: 99%

Simulation Inquisition of Noise Dissolving Algorithm Hinge on TTSD filter for RIIN Situation

Patanavijit

Thakulsukanant

2021

ECTI-CIT

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This primary aim of this philosopher paper investigates the efficacy of the noise dissolving algorithm hinge on TTSD (Triple Threshold Statistical Detection) filter that has been originated since 2018 is one of the highest efficacy for dissolving RIIN (Random-Intensity Impulse Noise), exclusively at dense distribution. As a results, there are three essential contributions: the exhaustive explanation of the TTSD filter algorithm and its computation examples, the calculation simulation of noise apprehension correctness and overall comparative simulation of noise dissolving effectiveness. For TTSD filter, three malleable offsets that are the complementary requirement are employed in the TTSD filter that can adequately resolve the limitation of the antecedent noise dissolving algorithms. The first malleable offset is calculated for determining the noise characteristic of all elements by using the mathematical verification. Next, the second malleable offset is calculated for determining the another noise characteristic by using the normal distribution mathematical verification (the average value and standard deviation value). Later, the third malleable offset is calculated for determining the another noise characteristic by using the quartile mathematical verification (median value). In the simulation inquisition, the bountiful standard portraits that are desecrated by RIIN (Random Intensity Impulse Noise) with many dense distributions are experimented by noise dissolving algorithm hinge on TTSD in both noise segregation and noise dissolving perspective.

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“…The Gaussian filter (e.g. Gaussian Seddik & Ben Braiek 2012;Pourebrahimi & C. A. van der Lubbe 2009) is a commonly used low-pass linear filter that reduces high frequency signals, and the median filter is a non-linear filter where the central pixel is replaced by the median of its neighboring pixels within the kernel (Zhu & Huang 2012;Charmouti et al 2017). In comparison with Gaussian filter, the median filter is better at preserving edges.…”

Section: Introductionmentioning

confidence: 99%

Learning to denoise astronomical images with U-nets

Vojtekova

Lieu

Valtchanov

et al. 2020

Monthly Notices of the Royal Astronomical Society

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Astronomical images are essential for exploring and understanding the universe. Optical telescopes capable of deep observations, such as the Hubble Space Telescope, are heavily oversubscribed in the Astronomical Community. Images also often contain additive noise, which makes de-noising a mandatory step in post-processing the data before further data analysis. In order to maximise the efficiency and information gain in the post-processing of astronomical imaging, we turn to machine learning. We propose Astro U-net, a convolutional neural network for image de-noising and enhancement. For a proof-of-concept, we use Hubble space telescope images from WFC3 instrument UVIS with F555W and F606W filters. Our network is able to produce images with noise characteristics as if they are obtained with twice the exposure time, and with minimum bias or information loss. From these images, we are able to recover $95.9\%$ of stars with an average flux error of $2.26\%$. Furthermore the images have, on average, 1.63 times higher signal-to-noise ratio than the input noisy images, equivalent to the stacking of at least 3 input images, which means a significant reduction in the telescope time needed for future astronomical imaging campaigns.

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Progression approach for image denoising

Cited by 3 publications

References 27 publications

Image Denoising Based on Implementing Threshold Techniques in Multi-Resolution Wavelet Domain and Spatial Domain Filters

Image Denoising Based on Implementing Threshold Techniques in Multi-Resolution Wavelet Domain and Spatial Domain Filters

Simulation Inquisition of Noise Dissolving Algorithm Hinge on TTSD filter for RIIN Situation

Learning to denoise astronomical images with U-nets

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