Enhancing rice leaf images: An overview of image denoising techniques

• URL: http://arxiv.org/abs/2511.00046v1
• Date: Tue, 28 Oct 2025 08:50:17 GMT
• Title: Enhancing rice leaf images: An overview of image denoising techniques
• Authors: Rupjyoti Chutia, Dibya Jyoti Bora,
• Abstract summary: Image enhancement is essential for rice leaf analysis, aiding in disease detection, nutrient deficiency evaluation, and growth analysis.<n>Image filters, generally employed for denoising, transform or enhance visual characteristics like brightness, contrast, and sharpness.<n>This work provides an extensive comparative study of well-known image-denoising methods combined with CLAHE (Contrast Limited Adaptive Histogram Equalization) for efficient denoising of rice leaf images.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Digital image processing involves the systematic handling of images using advanced computer algorithms, and has gained significant attention in both academic and practical fields. Image enhancement is a crucial preprocessing stage in the image-processing chain, improving image quality and emphasizing features. This makes subsequent tasks (segmentation, feature extraction, classification) more reliable. Image enhancement is essential for rice leaf analysis, aiding in disease detection, nutrient deficiency evaluation, and growth analysis. Denoising followed by contrast enhancement are the primary steps. Image filters, generally employed for denoising, transform or enhance visual characteristics like brightness, contrast, and sharpness, playing a crucial role in improving overall image quality and enabling the extraction of useful information. This work provides an extensive comparative study of well-known image-denoising methods combined with CLAHE (Contrast Limited Adaptive Histogram Equalization) for efficient denoising of rice leaf images. The experiments were performed on a rice leaf image dataset to ensure the data is relevant and representative. Results were examined using various metrics to comprehensively test enhancement methods. This approach provides a strong basis for assessing the effectiveness of methodologies in digital image processing and reveals insights useful for future adaptation in agricultural research and other domains.

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