Istiaq Ahmed
- Research Assistant, HCI Wing
Research Interests
mHealth, Human-Computer Interaction, Cross-Platform App/Website Development
Conference Paper2025
Adaptive Denoising Filter Selection for Preprocessing Low-Light Images via Feature-Based Machine Learning Noise Classification System
SM A Sayed Al Banna, Istiaq Ahmed, Ashraful Islam, Rashedur Rahman, Faiza Omar Arpita, Saadia Binte Alam
2025 IEEE 23rd Student Conference on Research and Development (SCOReD)
In: 2025 IEEE 23rd Student Conference on Research and Development (SCOReD)
IEEE, pp. 1-6
Abstract
Image noise degrades computer vision performance in critical fields like medical imaging and autonomous systems. A key challenge is that different noise types require specific treatments, making universal denoising filters ineffective. Non-blind denoising is impractical without prior noise knowledge, while deep learning solutions are often computationally costly for preprocessing. We propose a robust machine learning framework for noise classification based on feature extraction. Our methodology synthesizes a diverse dataset by applying five noise types—Gaussian, Poisson, Salt & Pepper, Quantization, and Periodic—to clean images from the BSDS500 benchmark. From each image, an 8-dimensional feature vector is extracted, capturing distinct statistical, textural, and frequency-domain characteristics. A Random Forest (RF) classifier, optimized via hyperparameter tuning, is trained on this feature set. The model achieves 91% classification accuracy, demonstrating the potency of these features, with analysis revealing the paramount importance of textural descriptors from the Gray-Level Co-occurrence Matrix (GLCM). This work establishes that feature-based machine learning provides a highly accurate, efficient, and interpretable solution for this pivotal preprocessing step in computer vision pipelines.
Keywords
Noise (video)Feature selectionPreprocessorNoise reductionPattern recognition (psychology)Filter (signal processing)Feature (linguistics)Random forestSupport vector machine
CCDS Authors
Ashraful Islam, PhD
- Center Director, CCDS
- Director, HCI Wing
Assistant Professor
Department of Computer Science and Engineering
Independent University, Bangladesh (IUB)Research Interests
Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact
Md. Rashedur Rahman, D. Eng.
- Co-Director, MIRA & NEST Wings
Assistant Professor
Department of Computer Science and Engineering
Independent University, Bangladesh
Saadia Binte Alam, PhD
- Director, MIRA & WiSE Wings
Associate Professor
Department of Computer Science and Engineering
Independent University, Bangladesh
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