Citation :

Pinki Devi, Mirzanur Rahman, Dankan Gowda V, Uzzal Sharma, Manjunath R.N, "Performance Comparison of Image Processing-Based Contrast Enhancement Algorithms on Microscopic Longitudinal Muga Silk Images," International Journal of Electronics and Communication Engineering, vol. 13, no. 5, pp. 139-150, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I5P113

Abstract

The "Queen of Textiles" alludes to silk, which is known for its high-quality fabric. Assam contributes for 95% of the country's Muga silk production. The term silk originates from a type of worm commonly known as the silkworm. Sericulture is the technique of raising silkworms to produce silk. The purity of silk can be tested using traditional methods; in the present era, we are contemplating digital techniques. In recent years, image processing techniques have been coupled with Artificial Intelligence, Machine Learning, and Deep Learning to make the purity assessment procedure even easier. To evaluate purity, a good-quality image is needed. We can improve image quality by using image processing and computer vision algorithms. Muga silk has its own exquisite qualities and complex textures; silk images often face unique challenges when it comes to contrast enhancement. It has additional hurdles when categorizing different types of silk due to image quality. In this study, we compare two image contrast enhancement approaches on a small dataset of specially recorded microscopic longitudinal Muga silk photographs. The current work employs image contrast enhancement approaches such as Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The goal of this research is to find the best strategies for increasing contrast while preserving the delicate features and distinctive attributes of microscopic longitudinal Muga silk weaves. Our experimental results show that CLAHE is a good strategy for improving the visual quality of microscopic longitudinal Muga silk images.

Keywords

Muga Silk, Antheraea assamensis, Contrast enhancement, Histogram Equalization(HE), Contrast Limited Adaptive Histogram Equalization(CLAHE), Microscopic image, longitudinal view.

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