International Journal of Advanced Technology and Engineering Exploration ISSN (Print): 2394-5443    ISSN (Online): 2394-7454 Volume-12 Issue-123 February-2025
  1. 4774
    Citations
  2. 2.8
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Texture edge smoothing and sharpening algorithm based on iterative non-local guided model

Ahmad Fauzan Kadmin1, 2, Rostam Affendi Hamzah1, 2, Nasharuddin Zainal3, Shamsul Fakhar Abd Gani1, 2 and Nabil Jazli4

Centre for Telecommunication Research and Innovation (CETRI),Universiti Teknikal Malaysia Melaka, Durian Tunggal, 76100 Melaka,Malaysia1
Fakulti Teknologi Dan Kejuruteraan Elektronik Dan Komputer,Universiti Teknikal Malaysia Melaka, Durian Tunggal, 76100 Melaka,Malaysia2
Faculty of Engineering and Built Environment,Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor,Malaysia3
IT Support Department, Amcorp Services Sdn Bhd,Petaling Jaya 46050 Selangor,Malaysia4
Corresponding Author : Ahmad Fauzan Kadmin

Recieved : 02-August-2024; Revised : 14-January-2025; Accepted : 22-January-2025

Abstract

Image smoothing and sharpening are crucial operations in image processing, underpinning a wide array of applications across computer vision, medical imaging, and remote sensing. These processes are essential for delineating object details from noise, which is vital in fields such as graphics, computational photography, and computer vision. Despite their importance, achieving an ideal balance between smoothing and sharpening is challenging due to trade-offs and the presence of various types of noise and irregularities in real-life images. Traditional methods, such as Gaussian or median filtering (MF) for smoothing and Laplacian or unsharp masking for sharpening, often introduce artifacts or fail to preserve crucial details. This work proposes a cutting-edge image filter that used iterative non-local guided model (inLG), designed to be edge-aware and minimize halo artifacts. The primary objective is to enhance texture edge smoothing performance while preserving essential details and sharpening critical features in digital images. The filter's effectiveness is demonstrated through applications in image enhancement, evaluated through quantitative and qualitative, confirming its capability. The experimental results demonstrate the algorithm's superior performance, achieving a mean squared error (MSE) of 0.276, a peak signal-to-noise ratio (PSNR) of 59.82 dB, and a structural similarity index (SSIM) of 0.999. These results surpass traditional methods, offering a balanced trade-off between edge preservation and noise reduction.

Keywords

Image processing, Image smoothing, Image sharpening, Edge-aware filtering, Noise reduction, Detail enhancement.

Cite this article

Kadmin AF, Hamzah RA, Zainal N, Gani SFA, Jazli N. Texture edge smoothing and sharpening algorithm based on iterative non-local guided model. International Journal of Advanced Technology and Engineering Exploration. 2025;12(123):219-236. DOI : 10.19101//IJATEE.2024.111101397

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