水稻叶片图像增强：图像去噪技术综述
Enhancing rice leaf images: An overview of image denoising techniques
AI总结：
本文综述并比较了多种经典图像去噪方法结合CLAHE技术在水稻叶片图像增强中的应用，通过多指标实验验证了其有效性，为农业图像处理提供了方法评估基础。
摘要：
数字图像处理涉及使用先进的计算机算法对图像进行系统处理，在学术和实践领域均受到广泛关注。图像增强是图像处理链中至关重要的预处理阶段，可改善图像质量并突出特征，使后续任务（分割、特征提取、分类）更加可靠。图像增强对于水稻叶片分析至关重要，有助于病害检测、营养缺乏评估和生长分析。去噪及随后的对比度增强是主要步骤。通常用于去噪的图像滤波器可变换或增强亮度、对比度和清晰度等视觉特征，在提高整体图像质量和提取有用信息方面发挥着关键作用。本研究对知名的图像去噪方法与 CLAHE（对比度受限自适应直方图均衡化）的组合进行了广泛的比较研究，以实现水稻叶片图像的高效去噪。实验在水稻叶片图像数据集上进行，以确保数据的相关性和代表性。使用各种指标检查结果，以全面测试增强方法。该方法为评估数字图像处理中方法的有效性提供了坚实基础，并揭示了有助于未来在农业研究及其他领域进行适应的有用见解。
英文摘要：
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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