Authors:
R. Remya, O. Jeba Singh, J. Angelin Jeba, S. Rubin Bose, R. Regin, M. Mohamed Sameer Ali
Addresses:
Department of Electronics and Communication Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India. Centre for Academic Research, Alliance University, Bengaluru, Karnataka, India. Department of Electronics and Communication Engineering, S.A. Engineering College, Chennai, Tamil Nadu, India. School of Computer Science Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India. Department of Research and Development, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India.
Abstract:
Image denoising is very important for getting better image quality so that you can analyze and understand it correctly. Various evaluation metrics are used to assess how well filtering techniques work and provide reliable quality assessments. This study introduces an innovative denoising technique for versatile images across various application domains. The filtered output generated by the proposed technique serves as a crucial preprocessing step for subsequent processes, including segmentation, feature extraction, and classification, where noise-free data markedly improves performance. Researchers use well-known quantitative measures, such as Peak Signal-to-Noise Ratio (PSNR), Normalized Absolute Error (NAE), and Structural Similarity Index Measure (SSIM), to assess the quality of denoised images. Researchers perform experimental analysis on a wide range of image types, including remote sensing, medical, and standard benchmark images such as Barbara, Cameraman, vegetable, and scenic. The results show that the new enhanced discrete wavelet transform (DWT)–based method outperforms other denoising methods for filtering. The improved DWT method, in particular, provides about 0.2% better filtering efficiency across all datasets tested. In general, the results show that the proposed denoising framework consistently improves image quality and effectively supports advanced image processing tasks in medical imaging, remote sensing, and general computer vision applications.
Keywords: Image Filtering; Image Denoising; Rician Noise; Similarity Index Measure (SSIM); Normalized Absolute Error (NAE); Computer Vision; Discrete Wavelet Transform (DWT).
Received on: 18/04/2025, Revised on: 05/06/2025, Accepted on: 06/08/2025, Published on: 01/03/2026
DOI: 10.64091/ATIIR.2026.000279
Ave Trends in Intelligent Informatics Reports, 2026 Vol. 1 No. 1 , Pages: 37–51