• SK . Faiz Ahmed
• P. Venkata Krishna

Image denoising remains a cornerstone challenge in computer vision and image processing, essential for enhancing visual quality in applications ranging from medical imaging to autonomous systems. This perspective paper synthesizes three seminal works on geometric pixel location encoding for color and grayscale denoising, BM3D hybrids, and genetic algorithm (GA) optimizations, highlighting their contributions to noise suppression while preserving structural integrity. The geometric encoding approach involves initial filtering of noisy images, followed by pixel rearrangement through diagonal rotation and even-row/column shifts for each RGB channel, enabling a second-pass denoising that improves Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). For grayscale images, this is augmented with BM3D filtering, yielding superior Mean Squared Error (MSE) and Kurtosis-Variance Signal-to-Noise Deviation (KVSD) metrics. The GA-based method leverages pixel lattice representations with customized crossover and mutation operators, guided by a Markov Random Field fitness function, to iteratively refine denoised outputs and traditional filters. Experimental results demonstrate robustness across noise levels, with the hybrids reducing computation time while elevating PSNR and SSIM. Limitations include dependency on known noise models and computational overhead for high-resolution images. Looking forward, integrating these hybrids with 2023-2024 advancements like transformers for non-local dependencies, diffusion models for generative refinement, reinforcement learning (RL) for GA parameter tuning, and edge AI for real-time deployment promises blind, multimodal denoising. This vision outlines a research agenda to evolve these methods into efficient, adaptive systems for diverse data streams, fostering innovations in real-world vision tasks.

Image Denoising; Geometric Encoding; BM3D; Genetic Algorithm (GA); Hybrid Denoising; Spatial Reconfiguration; Transform-Domain Filtering; Adaptive Optimization; PSNR; SSIM; MSE; Structural Fidelity; Kodak Dataset; Color Image Denoising; Grayscale Image Denoising; Deep Learning; Transformer Models; Diffusion Models; Reinforcement Learning; Edge AI; Computer Vision; Real-Time Denoising.

"A Vision for AI-Enhanced Real-Time Image Denoising in the Era of Multimodal Intelligence", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 9, page no.c155-c161, September-2026, Available :http://www.jetir.org/papers/JETIR2609217.pdf

"A Vision for AI-Enhanced Real-Time Image Denoising in the Era of Multimodal Intelligence", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 9, page no. ppc155-c161, September-2026, Available at : http://www.jetir.org/papers/JETIR2609217.pdf