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Kobra, Mst Jannatul
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Engineering

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A Novel Hybrid Framework for Noise Estimation in High-Texture Images using Markov, MLE, and CNN Approaches Kobra, Mst Jannatul; Md Owahedur Rahman; Arman Mohammad Nakib
Scientific Journal of Engineering Research Vol. 1 No. 2 (2025): April
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i2.2025.25

Abstract

The assessment of complex noise in textured images requires a method which uses both Markov processes together with Maximum Likelihood Estimation and Convolutional Neural Networks. The evaluation of noise through traditional methods does not deliver acceptable results during preservation of image characteristics in areas with challenging texture patterns. Through Maximum Likelihood Estimation (MLE) probabilistic refinement together with Convolutional Neural Networks (CNNs) features the proposed model applies Markov processes to maintain spatial dependencies that provide accurate denoising with protected image quality. Using CNN-based denoising together with Gaussian filtering creates superior outcomes for imaging perception than individual methods during Edge Preservation Index (EPI) and Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) assessment. The experimental results show a 24.85 dB PSNR value together with 0.92 SSIM integrity and EPI quality of 0.85 for effective hybrid model noise reduction. The research utilizes Markov processes and MLE together with Convolutional Neural Networks to develop an all-encompassing approach for cleaning texturized complex images which could serve multiple image types including those from medical contexts and satellites and digital photographs.
Effectiveness of Fourier, Wiener, Bilateral, and CLAHE Denoising Methods for CT Scan Image Noise Reduction Kobra, Mst Jannatul; Nakib, Arman Mohammad; Mweetwa, Peter; Rahman, Md Owahedur
Scientific Journal of Engineering Research Vol. 1 No. 3 (2025): July
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i3.2025.27

Abstract

The proper reduction of noise inside CTscan Images remains crucial to achieve both better diagnosis results and clinical choices. This research analyzes through quantitative metrics the effectiveness of four popular noise reduction methods which include Fourier-based denoising and Wiener filtering as well as bilateral filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE) applied to more than 500 CTscan Images. The investigated methods were assessed quantitatively through Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) while Mean Squared Error (MSE) served as the additional metric for evaluation. The evaluated denoising methods revealed bilateral filtering as the best technique based on its 50.37 dB PSNR and 0.9940 SSIM together with its 0.5967 MSE. Denoising with Fourier-based methods succeeded in removing high-frequency noise however it produced PSNR of 25.89 dB along with SSIM of 0.8138 while maintaining MSE at 167.4976 indicating lost crucial Image information. The performance balance of Wiener filtering resulted in 40.87 dB PSNR and 0.9809 SSIM and 5.3270 MSE that outperformed Fourier denoising in SSIM yet demonstrated higher MSE. CLAHE produces poor denoising outcomes because it achieves the lowest PSNR of 21.51 dB together with SSIM of 0.5707, and the maximum MSE of 459.1894 while creating undesirable artifacts. This research stands out through a full evaluation of four denoising techniques on a big dataset to create more precise analysis than prior research. The research results show bilateral filtering to be the most reliable technique for CTscan Image noise reduction when maintaining picture quality and thus represents a suitable choice for clinical use. This research adds new information to medical imaging research about quality enhancement which directly benefits clinical diagnostics and therapeutic planning.
Hybrid K-means, Random Forest, and Simulated Annealing for Optimizing Underwater Image Segmentation Kobra, Mst Jannatul; Rahman, Md Owahedur; Nakib, Arman Mohammad
Scientific Journal of Engineering Research Vol. 1 No. 4 (2025): October Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i4.2025.46

Abstract

The process of underwater image segmentation is also very difficult because the data collected by the underwater sensors and cameras is of very high complexity, and much data is generated and in that case, the data is not well seen, the color is distorted, and the features overlap. Current solutions, including K-means clustering and Random Forest classification, are unable to partition complex underwater images with high accuracy, or are unable to scale to large datasets, although the possibility of dynamically optimizing the number of clusters has not been fully explored. To fill these gaps, this paper advises a hybrid solution that combines K-means clustering, Random Forest classification and the Simulated Annealing optimization as a complete end to end system to maximize the efficiency and accuracy of segmentation. K-means clustering first divides images based on pixel intensity, Random Forest narrows its segmentation of images with features like texture, color and shape, and Simulated Annealing determines the desired number of clusters dynamically to segment images with minimal segmentation error. The segmentation error of the proposed method was 30 less than the baseline K-means segmentation accuracy of 65 percent and the proposed method segmentation accuracy was 95% with an optimal cluster number of 10 and a mean error of 7839.22. This hybrid system offers a large-scale, scalable system to underwater image processing that is robust and has applications in marine biology, environmental research, and autonomous underwater system exploration.
Co-Authors Arman Mohammad Nakib Md Owahedur Rahman Mweetwa, Peter Nakib, Arman Mohammad Rahman, Md Owahedur