Correlative Analysis of Denoising Methods in Spectral Images Embedded with Different Noises

Annam, Sangeetha; Singla, Ashish

doi:10.1109/pdgc50313.2020.9315749

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…However, they lost much information during the separation process that make the denoising process a challenging task. Furthermore, the authors of [ 12 ] performed a correlative examination of various noise removal techniques against different operators in spectral images. These images are presented with various kinds of noise and further operators are employed to denoise the corresponding image.…”

Section: Literature Reviewmentioning

confidence: 99%

Signal-to-Noise Ratio Comparison of Several Filters against Phantom Image

Ṣiddiqi

Alhwaiti

2022

Journal of Healthcare Engineering

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Image denoising methods are important in order to diminish various kinds of noises, which are presented either capturing the image or distorted during image transmission. Signal-to-noise ratio (SNR) is one of the main barriers which avoids the theoretical observations to be accomplished in practice. In this study, we have utilized various kinds of filtering operators against three various noises, which are the signal-to-noise ratio comparison against the phantom image in spatial and frequency domain. In frequency domain, the average filter is used to smooth the image and frequency domain, and Gaussian low-pass filter is applied with empirically determined cutoff frequency. This work has six major parts such as applying average filter, determining the SNR of region of interest, transforming the image in frequency domain by discrete Fourier transform, obtaining the rectangular Gaussian low-pass filter along with a cutoff frequency, multiplying them, and carrying out the inverse Fourier transform. These steps are repeated accordingly until the resulting image SNR is equal to or greater than the spatial domain SNR. In order to achieve the goal of this study, we have analyzed the proposed approach against some of complex phantom images. The performances of these filters are compared against signal-to-noise ratio.

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Section: Literature Reviewmentioning

confidence: 99%

Signal-to-Noise Ratio Comparison of Several Filters against Phantom Image

Ṣiddiqi

Alhwaiti

2022

Journal of Healthcare Engineering

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“…The noise must be identified based on its pattern and probabilistic properties. Image and signal data include a wide variety of noise [4][5][6]. Gaussian, Salt & Pepper, Poisson, impulse, and Speckle noise are the most common types of noise that distort images.…”

Section: Introductionmentioning

confidence: 99%

Effect of denoising methods for hyperspectral images classification: DnCNN, NGM, CSF, BM3D and Wiener

Günen

2023

Mersin Photogrammetry Journal

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Hyperspectral images contain a large amount of information and are composed of several spectral bands. These data are used to classify pixels for land use/cover analysis, which is a popular subject, particularly in remote sensing. Because of noise caused by systematic and random errors, these data cannot be presented to end users most of the time. In this paper, Pavia university’s hyperspectral dataset with Gaussian, salt & pepper, poisson, and speckle noise were denoised using DnCNN, NGM, CSF, BM3D, and Wiener. Then, denoised data was classified using the k-nearest neighbor method. Following that, statistical and visual performance comparisons were performed for classified data. The BM3D method was statistically the most successful, whereas the NGM method was the least successful. The validated instruments not provide effective results when it came to denoising salt & pepper noise, but they managed to produce outstanding results when it came to denoising poisson noise.

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