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.

翻译：噪声作为图像中的非期望成分，可能导致图像在传输或采集过程中质量退化。图像去噪至今仍是具有挑战性的任务。数字图像处理是数字信号处理的重要组成部分。图像处理领域存在多种算法，可应用于图像或输入数据集以获得关键结果。在图像处理研究中，在进一步分析前去除图像噪声至关重要。后去噪处理能提升图像清晰度，从而在医学影像、卫星图像和雷达应用等领域实现更精准的解析与分析。尽管现有算法众多，但每种算法都有其特定的假设条件、优势与局限性。本文旨在评估八类噪声图像上不同滤波技术的有效性，通过峰值信噪比性能指标，系统评估维纳滤波、中值滤波、高斯滤波、均值滤波、低通滤波、高通滤波、拉普拉斯滤波及双边滤波等方法。研究通过将多种滤波器应用于不同类型的噪声，揭示了不同滤波器对噪声模型的影响机制。此外，本研究还能根据具体应用场景，辅助确定特定噪声模型下最优的滤波策略。