On the reduction of mixed Gaussian and impulsive noise in heavily corrupted color images

Smolka, Bogdan; Kusnik, Damian; Radlak, Krystian

doi:10.1038/s41598-023-48036-1

Cited by 2 publications

(1 citation statement)

References 121 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Denoising is an intricate process that aims to remove noise from an image while maintaining its quality and intricate details. The primary classifications of noise in images include Impulse Noise (IN) [2][3] [4], Additive White Gaussian Noise (AWGN) [3] [4], and Speckle Noise [5][6] [7], and Speckle Noise. There are two types of impulse noise: Salt and Pepper Noise (SPN) [8] [9][10] [11] and Random Valued Impulse Noise (RVIN) [12] [13].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Image Clarity with the Combined Use of REDNet and Attention Channel Module

Halim,

Putra Kusuma

2024

IJCDS

View full text Add to dashboard Cite

The primary aim of our study is to improve the efficacy of image denoising, specifically in situations when there is a limited availability of data, such as the BSD68 dataset. Insufficient data presents a challenge in achieving optimal outcomes due to the complexity involved in constructing models. In order to tackle this difficulty, we provide a method that incorporates Channel Attention, Batch Normalization, and Dropout approaches into the current REDNet framework. Our investigation indicates enhancements in performance parameters, such as PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index), across various levels of noise. With a noise level of 15, we obtained a Peak Signal-to-Noise Ratio (PSNR) of 34.9858 dB and a Structural Similarity Index (SSIM) of 0.9371. At a noise level of 25, our tests yielded a PSNR of 31.7886 decibels and an SSIM of 0.8876. In addition, at a noise level of 50, we achieved a Peak Signal-to-Noise Ratio (PSNR) of 27.9063 decibels and a Structural Similarity Index (SSIM) of 0.7754. The incorporation of Channel Attention, Batch Normalization, and Dropout has been demonstrated to be a crucial element in enhancing the efficacy of image denoising. The Channel Attention approach enables the model to choose and concentrate on crucial information inside the image, while Batch Normalization and Dropout techniques provide stability and mitigate overfitting issues throughout the training process. Our research highlights the effectiveness of these three strategies and emphasizes their integration as a novel way to address the constraints presented by the scarcity of data in image denoising jobs. This emphasizes the significant potential in creating dependable and effective image denoising methods when dealing with circumstances when there is a limited dataset.

show abstract

Section: Introductionmentioning

confidence: 99%

Enhancing Image Clarity with the Combined Use of REDNet and Attention Channel Module

Halim,

Putra Kusuma

2024

IJCDS

View full text Add to dashboard Cite

show abstract

Adaptive median filter salt and pepper noise suppression approach for common path coherent dispersion spectrometer

Guan,

Liu,

Chen

et al. 2024

Sci Rep

View full text Add to dashboard Cite

The Common-path Coherent-dispersion Spectrometer (CODES), an exoplanet detection instrument, executes high-precision Radial Velocity (RV) inversions by recording the phase shifts of interference fringes. Salt-and-pepper noise caused by factors such as improper operation of the CCD probe/analog-to-digital converter and strong dark currents may interfere with the phase information of the fringe. This lowers the quality of the interfering fringe image and significantly interferes with the RV’s inversion. In this study, an adaptive median filtering algorithm (CODESmF) based on submaximum and subminimum values is designed to eliminate the interference fringe image's salt-and-pepper noise as well as to reduce RV error. This allows the interference fringe image's phase information to be retained more completely. The algorithm consists of two major modules. Pixel Sub-extreme-based Filtered Noise Monitoring Module: discriminates signal pixels and noise pixels based on the submaximum and subminimum values of the pixels in the filtering window. Adaptive Median Filter Noise Suppression Module: the signal pixel is kept at the original value output, the noise pixel serves as the filtering window's center pixel, and the adaptive median filtering procedure is repeated numerous times with various filtering window sizes. According to the experimental findings, the CODESmF outperforms comparable algorithms and works better at recovering interference fringes. More than 90% of the phase/RV error caused by salt-and-pepper noise is typically eliminated by the CODESmF algorithm, and in certain circumstances, it can even remove roughly 98% of the phase error.

show abstract

On the reduction of mixed Gaussian and impulsive noise in heavily corrupted color images

Cited by 2 publications

References 121 publications

Enhancing Image Clarity with the Combined Use of REDNet and Attention Channel Module

Enhancing Image Clarity with the Combined Use of REDNet and Attention Channel Module

Adaptive median filter salt and pepper noise suppression approach for common path coherent dispersion spectrometer

Contact Info

Product

Resources

About