ABSTRACT Digital image processing is a mechanism for analysing and modifying the image in order to improve the quality and also to manage the unwanted involvement of noises. In image processing, noise is characterized as an unwanted disturbance which occurs while capturing the actual image thus affecting the quality of the image. Hence, noise formation is considered as a perilous issue and the reduction of noise is considered as an awkward process. Nowadays, almost in all fields of science and technology, digital image processing is increasing rapidly, so there arises the need for de-noising to cure the noised image. The main objective of this paper is to overcome the issue of noise and also to increase the quality and pixel value of the image. An advanced methodology known as collaborative filtering and Pillar K-Mean clustering is discussed in this paper to overcome the abovementioned problem. Initially, distinct pure images are taken as the dataset and three types of noises are added to the corresponding image to make it as a noised one. Hence, the unspecified noise is resolved on the basis of a hybrid combination of algorithms of collaborative filtering with the image inpainting method. Sequentially, the low-density noises, such as random noise and poison noise, are recovered by the implementation of collaborative filtering, and the high-density salt and pepper noise are recovered by the image inpainting method. Based on the GLCM (Grey Level Co-occurrence Matrix) feature, the normal image and the noised image are used for the clustering process. Then the de-noised image is evaluated to find the efficiency on the basis of few parameters such as SNR (Signal to Noise Ratio), MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio) and SSI (Structural Similarity Index). Accordingly, the evaluated images are further withstood for clustering to differentiate the noises by applying the proposed clustering methodology. Then the evaluated images are verified on the basis of a few parameters such as Silhouette Width, Davies–Bouldin Index and Dunn Index. The proposed methodology is run on the platform of Mat Lab. Finally, the proposed methodology is considered as an efficient method for settling the issue in digital image de-noising.

中文翻译：

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使用带有 Pillar K-Mean 聚类的协同修复过滤优化图像处理中的噪声降低

摘要 数字图像处理是一种分析和修改图像的机制，以提高质量并管理不需要的噪声。在图像处理中，噪声被表征为在捕获实际图像时发生的不想要的干扰，从而影响图像质量。因此，噪音的形成被认为是一个危险的问题，而噪音的减少被认为是一个尴尬的过程。如今，几乎在所有科学技术领域，数字图像处理都在快速增长，因此出现了去噪以治愈噪声图像的需求。本文的主要目标是克服噪声问题，并提高图像的质量和像素值。本文讨论了一种称为协同过滤和 Pillar K-Mean 聚类的高级方法来克服上述问题。最初，将不同的纯图像作为数据集，并将三种类型的噪声添加到相应的图像中，使其成为有噪声的图像。因此，基于协同过滤算法与图像修复方法的混合组合来解决未指定的噪声。依次通过协同过滤的实现对随机噪声、毒物噪声等低密度噪声进行恢复，通过图像修复方法对高密度椒盐噪声进行恢复。基于GLCM（灰度共生矩阵）特征，将正常图像和噪声图像用于聚类过程。然后根据 SNR（信噪比）、MSE（均方误差）、PSNR（峰值信噪比）和 SSI（结构相似性指数）等几个参数评估去噪图像以找到效率. 因此，通过应用所提出的聚类方法，评估的图像进一步经受聚类以区分噪声。然后根据轮廓宽度、Davies-Bouldin 指数和邓恩指数等几个参数对评估的图像进行验证。所提出的方法在 Mat Lab 平台上运行。最后，所提出的方法被认为是解决数字图像去噪问题的有效方法。PSNR（峰值信噪比）和 SSI（结构相似性指数）。因此，通过应用所提出的聚类方法，评估的图像进一步经受聚类以区分噪声。然后根据轮廓宽度、Davies-Bouldin 指数和邓恩指数等几个参数对评估的图像进行验证。所提出的方法在 Mat Lab 平台上运行。最后，所提出的方法被认为是解决数字图像去噪问题的有效方法。PSNR（峰值信噪比）和 SSI（结构相似性指数）。因此，通过应用所提出的聚类方法，评估的图像进一步经受聚类以区分噪声。然后根据轮廓宽度、Davies-Bouldin 指数和邓恩指数等几个参数对评估的图像进行验证。所提出的方法在 Mat Lab 平台上运行。最后，所提出的方法被认为是解决数字图像去噪问题的有效方法。所提出的方法在 Mat Lab 平台上运行。最后，所提出的方法被认为是解决数字图像去噪问题的有效方法。所提出的方法在 Mat Lab 平台上运行。最后，所提出的方法被认为是解决数字图像去噪问题的有效方法。