New Real-Time High-Density Impulsive Noise Removal Method Applied to Medical Images

Alanazi, Turki M.; Berriri, Kamel; Albekairi, Mohammed; Atitallah, Ahmed Ben; Sahbani, Anis; Kaâniche, Khaled

doi:10.3390/diagnostics13101709

Cited by 3 publications

(2 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Prospectively speaking, salt and pepper noise can be produced by transmission, analog-to-digital conversion, and data acquisition errors. In particular, the processing of high-density salt and pepper noise is often required for medical images, for instance, magnetic resonance images [66] and X-ray images [67]. By preliminarily analyzing the processing results for medical brain magnetic resonance images, the ability of the proposed TSETMF algorithm to produce relatively stable and excellent noise reduction effects may be demonstrated.…”

Section: Contrastive Denoising With Continuous Increase In Noise Densitymentioning

confidence: 99%

High-Noise Grayscale Image Denoising Using an Improved Median Filter for the Adaptive Selection of a Threshold

Cao,

Liu

2024

Applied Sciences

View full text Add to dashboard Cite

Grayscale image processing is a key research area in the field of computer vision and image analysis, where image quality and visualization effects may be seriously damaged by high-density salt and pepper noise. A traditional median filter for noise removal may result in poor detail reservation performance under strong noise and the judgment performance of different noise characteristics has strong dependence and rather weak robustness. In order to reduce the effects of high-density salt and pepper noise on image quality when processing high-noise grayscale images, an improved two-dimensional maximum Shannon entropy median filter (TSETMF) is proposed for the adaptive selection of a threshold to enhance the filter performance while stably and effectively retaining the details of the images. The framework of the proposed improved TSETMF algorithm is designed in detail. The noise in images is filtered by means of automatically partitioning a window size, the threshold value of which is adaptively calculated using two-dimensional maximum Shannon entropy. The theoretical model is verified and analyzed through comparative experiments using three kinds of classical grayscale images. The experimental results demonstrate that the proposed improved TSETMF algorithm exhibits better processing performance than that of the traditional filter, with a higher suppression of high-density noise and denoising stability. This stronger ability while processing high-density noise is demonstrated by a higher peak signal-to-noise ratio (PSNR) of 24.97 dB with a 95% noise density located in the classical Lena grayscale image. The better denoising stability, with a noise density from 5% to 95%, is demonstrated by the minor decline in the PSNR of approximately 10.78% relative to a PSNR of 23.10 dB located in the classical Cameraman grayscale image. Furthermore, it can be advanced to promote higher noise filtering and stability for processing high-density salt and pepper noise in grayscale images.

show abstract

Section: Contrastive Denoising With Continuous Increase In Noise Densitymentioning

confidence: 99%

High-Noise Grayscale Image Denoising Using an Improved Median Filter for the Adaptive Selection of a Threshold

Cao,

Liu

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Computed tomography (CT) is a widely used medical imaging modality that precisely identifies anatomical structures and abnormalities [1]. Medical imaging has revolutionized the healthcare sector by assisting medical professionals in several ways, such as in disease diagnosis, treatment, and risk prediction [2].…”

Section: Introductionmentioning

confidence: 99%

An Image Denoising Technique Using Wavelet-Anisotropic Gaussian Filter-Based Denoising Convolutional Neural Network for CT Images

Abuya,

Rimiru,

Okeyo

2023

Applied Sciences

View full text Add to dashboard Cite

Denoising computed tomography (CT) medical images is crucial in preserving information and restoring images contaminated with noise. Standard filters have extensively been used for noise removal and fine details’ preservation. During the transmission of medical images, noise degrades the visibility of anatomical structures and subtle abnormalities, making it difficult for radiologists to accurately diagnose and interpret medical conditions. In recent studies, an optimum denoising filter using the wavelet threshold and deep-CNN was used to eliminate Gaussian noise in CT images using the image quality index (IQI) and peak signal-to-noise ratio (PSNR). Although the results were better than those with traditional techniques, the performance resulted in a loss of clarity and fine details’ preservation that rendered the CT images unsuitable. To address these challenges, this paper focuses on eliminating noise in CT scan images corrupted with additive Gaussian blur noise (AGBN) using an ensemble approach that integrates anisotropic Gaussian filter (AGF) and wavelet transform with a deep learning denoising convolutional neural network (DnCNN). First, the noisy image is denoised by AGF and Haar wavelet transform as preprocessing operations to eliminate AGBN. The DnCNN is then combined with AGF and wavelet for post-processing operation to eliminate the rest of the noises. Specifically, we used AGF due to its adaptability to edge orientation and directional information, which prevents blurring along edges for non-uniform noise distribution. Denoised images are evaluated using PSNR, mean squared error (MSE), and the structural similarity index measure (SSIM). Results revealed that the average PSNR value of the proposed ensemble approach is 28.28, and the average computational time is 0.01666 s. The implication is that the MSE between the original and reconstructed images is very low, implying that the image is restored correctly. Since the SSIM values are between 0 and 1.0, 1.0 perfectly matches the reconstructed image with the original image. In addition, the SSIM values at 1.0 or near 1.0 implicitly reveal a remarkable structural similarity between the denoised CT image and the original image. Compared to other techniques, the proposed ensemble approach has demonstrated exceptional performance in maintaining the quality of the image and fine details’ preservation.

show abstract

A Judicious way to restore random impulse noise using iterative weighted total variation diffusion technique

Pritamdas

2024

Pattern Anal Applic

View full text Add to dashboard Cite

New Real-Time High-Density Impulsive Noise Removal Method Applied to Medical Images

Cited by 3 publications

References 59 publications

High-Noise Grayscale Image Denoising Using an Improved Median Filter for the Adaptive Selection of a Threshold

High-Noise Grayscale Image Denoising Using an Improved Median Filter for the Adaptive Selection of a Threshold

An Image Denoising Technique Using Wavelet-Anisotropic Gaussian Filter-Based Denoising Convolutional Neural Network for CT Images

A Judicious way to restore random impulse noise using iterative weighted total variation diffusion technique

Contact Info

Product

Resources

About