Zhongliang Wei^1,*, Jianlong An¹, Chang Su²

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069335 - 10 November 2025

Abstract Images taken in dim environments frequently exhibit issues like insufficient brightness, noise, color shifts, and loss of detail. These problems pose significant challenges to dark image enhancement tasks. Current approaches, while effective in global illumination modeling, often struggle to simultaneously suppress noise and preserve structural details, especially under heterogeneous lighting. Furthermore, misalignment between luminance and color channels introduces additional challenges to accurate enhancement. In response to the aforementioned difficulties, we introduce a single-stage framework, M2ATNet, using the multi-scale multi-attention and Transformer architecture. First, to address the problems of texture blurring and residual noise, we design… More >

Apoorav Sharma¹, Ayush Dogra^2,*, Bhawna Goyal³, Archana Saini², Vinay Kukreja²

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 367-481, 2025, DOI:10.32604/cmc.2025.066481 - 29 August 2025

Abstract To enable proper diagnosis of a patient, medical images must demonstrate no presence of noise and artifacts. The major hurdle lies in acquiring these images in such a manner that extraneous variables, causing distortions in the form of noise and artifacts, are kept to a bare minimum. The unexpected change realized during the acquisition process specifically attacks the integrity of the image’s quality, while indirectly attacking the effectiveness of the diagnostic process. It is thus crucial that this is attended to with maximum efficiency at the level of pertinent expertise. The solution to these challenges… More >

Fawad Salam Khan^1,*, Noman Hasany², Sheikh Kamran Abid³, Muhammad Khurram⁴, Jerome Gacu^5,6,7, Cris Edward Monjardin⁸, Kevin Lawrence de Jesus⁷

Revue Internationale de Géomatique, Vol.34, pp. 415-432, 2025, DOI:10.32604/rig.2025.067026 - 29 July 2025

Abstract Noise present in remote sensing data creates obstacles to proper land use and land cover (LULC) classification methods. The paper evaluates machine learning (ML) denoising methods that adapt Raman spectroscopy’s spectral techniques to optimise remote sensing spectra for land-use/land-cover (LULC) mapping. A basic Raman spectroscopy model demonstrates that Savitzky-Golay (SG) filtering, Wavelet denoising, and basic 1D Convolutional Autoencoder have different effects on synthetic spectral features relevant to LULC classification. Savitzky-Golay filtering yielded the most efficient results, increasing classification accuracy from 0.71 (noisy) to 1.00 (denoised), resulting in perfect classification with zero errors and enhancing the More >

Guangfei Jia^*, Jinqiu Yang, Hanwen Liang

Structural Durability & Health Monitoring, Vol.19, No.4, pp. 1057-1072, 2025, DOI:10.32604/sdhm.2025.061805 - 30 June 2025

Abstract Considering the noise problem of the acquisition signals from mechanical transmission systems, a novel denoising method is proposed that combines Variational Mode Decomposition (VMD) with wavelet thresholding. The key innovation of this method lies in the optimization of VMD parameters K and using the improved Horned Lizard Optimization Algorithm (IHLOA). An inertia weight parameter is introduced into the random walk strategy of HLOA, and the related formula is improved. The acquisition signal can be adaptively decomposed into some Intrinsic Mode Functions (IMFs), and the high-noise IMFs are identified based on a correlation coefficient-variance method. Further noise… More > Graphic Abstract

Kangkang Zhao¹, Yaojie Chen^1,*, Jianbo Li²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 745-762, 2025, DOI:10.32604/cmc.2025.063906 - 09 June 2025

Abstract Traditional cameras inevitably suffer from motion blur when facing high-speed moving objects. Event cameras, as high temporal resolution bionic cameras, record intensity changes in an asynchronous manner, and their recorded high temporal resolution information can effectively solve the problem of time information loss in motion blur. Existing event-based deblurring methods still face challenges when facing high-speed moving objects. We conducted an in-depth study of the imaging principle of event cameras. We found that the event stream contains excessive noise. The valid information is sparse. Invalid event features hinder the expression of valid features due to… More >

Huiyao Dong¹, Igor Kotenko^2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4263-4285, 2025, DOI:10.32604/cmc.2025.062439 - 06 March 2025

Abstract The growing complexity of cyber threats requires innovative machine learning techniques, and image-based malware classification opens up new possibilities. Meanwhile, existing research has largely overlooked the impact of noise and obfuscation techniques commonly employed by malware authors to evade detection, and there is a critical gap in using noise simulation as a means of replicating real-world malware obfuscation techniques and adopting denoising framework to counteract these challenges. This study introduces an image denoising technique based on a U-Net combined with a GAN framework to address noise interference and obfuscation challenges in image-based malware analysis. The… More >

Wenkai Zhang^1,2, Hao Zhang^1,2, Xianming Liu¹, Xiaoyu Guo^1,2, Xinzhe Wang¹, Shuiwang Li^1,2,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2537-2554, 2025, DOI:10.32604/cmc.2024.059000 - 17 February 2025

Abstract Under low-illumination conditions, the quality of image signals deteriorates significantly, typically characterized by a peak signal-to-noise ratio (PSNR) below 10 dB, which severely limits the usability of the images. Supervised methods, which utilize paired high-low light images as training sets, can enhance the PSNR to around 20 dB, significantly improving image quality. However, such data is challenging to obtain. In recent years, unsupervised low-light image enhancement (LIE) methods based on the Retinex framework have been proposed, but they generally lag behind supervised methods by 5–10 dB in performance. In this paper, we introduce the Denoising-Distilled… More >

A. Robert Singh¹, Suganya Athisayamani², Gyanendra Prasad Joshi³, Bhanu Shrestha^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 299-327, 2025, DOI:10.32604/cmes.2024.055599 - 17 December 2024

Abstract Myocardial perfusion imaging (MPI), which uses single-photon emission computed tomography (SPECT), is a well-known estimating tool for medical diagnosis, employing the classification of images to show situations in coronary artery disease (CAD). The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks (CNNs). This paper uses a SPECT classification framework with three steps: 1) Image denoising, 2) Attenuation correction, and 3) Image classification. Image denoising is done by a U-Net architecture that ensures effective image denoising. Attenuation correction is implemented by a convolution neural network model that… More >

Dijun Rao^1,2,3,4, Min Huang^1,2,3,5, Xiuzhi Shi⁴, Zhi Yu^6,*, Zhengxiang He⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 187-217, 2024, DOI:10.32604/cmes.2024.051402 - 20 August 2024

Abstract The denoising of microseismic signals is a prerequisite for subsequent analysis and research. In this research, a new microseismic signal denoising algorithm called the Black Widow Optimization Algorithm (BWOA) optimized Variational Mode Decomposition (VMD) joint Wavelet Threshold Denoising (WTD) algorithm (BVW) is proposed. The BVW algorithm integrates VMD and WTD, both of which are optimized by BWOA. Specifically, this algorithm utilizes VMD to decompose the microseismic signal to be denoised into several Band-Limited Intrinsic Mode Functions (BLIMFs). Subsequently, these BLIMFs whose correlation coefficients with the microseismic signal to be denoised are higher than a threshold… More >

Yunjing Liu^1,, Erhu Zhang^1,2,,*, Jingjing Wang³, Guangfeng Lin², Jinghong Duan⁴

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1633-1653, 2024, DOI:10.32604/cmc.2024.052611 - 18 July 2024

Abstract Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research issue. Different from natural images, character images pay more attention to stroke information. However, existing models mainly consider pixel-level information while ignoring structural information of the character, such as its edge and glyph, resulting in reconstructed images with mottled local structure and character damage. To solve these problems, we propose a novel generative adversarial network (GAN) framework based on an edge-guided generator and a discriminator constructed by a dual-domain U-Net framework, i.e., EDU-GAN. Unlike existing frameworks, the generator introduces the… More >