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All Journal International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Scientific Journal of Computer Science
Rashid, Mizanur
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Image Denoising Method Based on 3D Block Matching with Harmonic Filtering in Transform Domain Rashid, Mizanur; Sayed, Abdullah Ibne; Rana, Md Masud
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 7 No. 1 (2026): INJIISCOM: VOLUME 7, ISSUE 1, JUNE 2026 (ONLINE FIRST)
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Today, I will explain an image denoising method based on 3D block matching with harmonic filtering in the transform domain. This topic is important because digital images are susceptible to noise during acquisition, storage, and transmission. Image denoising is crucial in pre-processing and is a key research area in digital image processing and computer vision. Traditional denoising techniques face limitations such as high computational complexity, so combining multiple methods is more effective. The integration of wave-domain harmonic filtering and 3D block matching (BM3D) introduces a new and efficient denoising algorithm. The Euclidean distance approach is used to group similar 2D image blocks into a 3D array. The inverse transformation reconstructs the image, followed by wavelet decomposition to filter high-frequency noise. To prevent edge blurring, the Laplacian-Gaussian algorithm is applied to refine the diffusion model. Finally, wavelet reconstruction is performed to approximate the original image. Experimental results demonstrate that this approach improves information protection and processing speed, making it highly effective in practice.
A GAT-Assisted Hybrid Reinforcement Learning and Swarm Intelligence Framework for Autonomous UAV Coordination Kobra, Mst Jannatul; Rahman, Md Owahedur; Rashid, Mizanur
Scientific Journal of Computer Science Vol. 1 No. 2 (2025): December
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i2.2025.316

Abstract

The autonomous UAV swarms have fundamental issues with strong coordination that arise under delays in communication, dynamic obstacles and noisy sensing environments, and the existing centralized or heuristic-based solutions are insufficient in addressing such issues. To cover this gap, this paper proposes a Graph Attention Network (GAT)-based Hybrid Reinforcement Learning and Swarm Intelligence Framework that can enable the communication-aware decentralized cooperation of UAVs. It is a multi-agent reinforcement learning and PSO, ACO, Differential Evolution, flocking behavior and Control Barrier Function-based safety correction, and GAT-inspired adaptive graph communication encoding. The results of the simulation of 18 episodes with 24 UAVs demonstrate that the reward, coverage, and collision were demonstrated to be improved by 32%, 27%, and 40% respectively as compared to a classical greedy baseline. The findings confirm the fact that the proposed hybrid GAT-RL architecture enables to promote significantly more scalability, safety, and real-time responsiveness of UAV swarms, which is a possibility on the path to large-scale autonomous aerial coordination.
Co-Authors Kobra, Mst Jannatul Rahman, Md Owahedur Rana, Md Masud Sayed, Abdullah Ibne