对比8种降噪算法在不同噪声下的表现，找最适合的处理方案
Analyzing Noise Models and Advanced Filtering Algorithms for Image Enhancement
- 测试了8种滤波器在8类噪声上的效果
- 用PSNR指标衡量，找到各噪声下最优滤波方法
- 适合图像处理初学者和需要选降噪方案的研究者
噪声是图像在采集或传输过程中导致退化的非期望成分。图像去噪仍是挑战性任务。数字图像处理作为数字信号处理的一部分，可对图像或输入数据集应用多种算法以获得重要结果。在图像处理研究中，为后续分析去除噪声至关重要。去噪后图像清晰度提升，有利于医学影像、卫星图像和雷达应用中的解读与分析。尽管已有众多算法，但每种算法均有其假设、优势与局限。本文旨在评估不同滤波技术在八类噪声图像上的有效性，比较维纳、中值、高斯、均值、低通、高通、拉普拉斯及双边滤波等方法，使用峰值信噪比（PSNR）作为性能指标。结果显示不同滤波器对各类噪声的影响差异显著，并可据此判断特定噪声模型下最合适的滤波策略。
原文摘要 · Abstract (English)
Noise, an unwanted component in an image, can be the reason for the degradation of Image at the time of transmission or capturing. Noise reduction from images is still a challenging task. Digital Image Processing is a component of Digital signal processing. A wide variety of algorithms can be used in image processing to apply to an image or an input dataset and obtain important outcomes. In image processing research, removing noise from images before further analysis is essential. Post-noise removal of images improves clarity, enabling better interpretation and analysis across medical imaging, satellite imagery, and radar applications. While numerous algorithms exist, each comes with its own assumptions, strengths, and limitations. The paper aims to evaluate the effectiveness of different filtering techniques on images with eight types of noise. It evaluates methodologies like Wiener, Median, Gaussian, Mean, Low pass, High pass, Laplacian and bilateral filtering, using the performance metric Peak signal to noise ratio. It shows us the impact of different filters on noise models by applying a variety of filters to various kinds of noise. Additionally, it also assists us in determining which filtering strategy is most appropriate for a certain noise model based on the circumstances.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。