Precision in Pixels: Enhancing Images of Medicinal and Aromatic Leaves
Received: 7 July 2025 | Revised: 13 August 2025 | Accepted: 22 August 2025 | Online: 8 December 2025
Corresponding author: B. S. Harish
Abstract
The application of digital image processing to medicinal and aromatic leaves has become increasingly important in industries such as pharmaceuticals, cosmetics, and food, as leaf quality directly influences their usability and market value. This study investigates various image enhancement techniques applied to the DIMPSAR and MAP 177 Medicinal Leaf Datasets to improve visual quality, facilitating better feature extraction and analysis. Methods such as contrast enhancement, edge enhancement, sharpening, noise reduction, morphological operations, High Dynamic Range (HDR) enhancement, denoising and restoration, and color enhancement are systematically evaluated based on performance metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Root MSE (RMSE). The results indicate that Linear Contrast Stretching (LCS) and median filtering are the most effective techniques, offering significant contrast and noise reduction improvements while preserving essential structural leaf details. Additionally, techniques such as white balance and unsharp masking enhance image consistency and sharpness, whereas Histogram Equalization (HE) and tone mapping introduce distortions that degrade image quality, limiting their applicability. Lastly, edge detection and morphological operations, primarily used for structure extraction, were found to amplify noise and distortions rather than improve image clarity.
Keywords:
medicinal leaves, aromatic leaves, leaf image enhancement, digital image processingDownloads
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