In this paper, The dissertation deals with the restoration techniques in Image Processing. Tychonoff Regularization method is one of the well known techniques followed in the last two decades. Recently the topological optimization has received the attention of many researchers in the recent past. More surprisingly, Image restoration and segmentation are achieved simultaneously with the help of topological gradient. In this method, the value of the diffusivity C in the smooth regions, the number of holes drilled in each iteration and the total numbers of holes are the main controlling parameters. A complete Mat lab code has been developed to implement our method. step 1 and 2 will give a brief history of Image Restoration techniques and step 3 will deal with topological optimization techniques and step 4 will discuss the implementation.

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Security plays a vital role in many applications. Intrusion detection is one of the securities which will be differing from conventional computer security attacks. Existing system analyze the behavior-rule specification-based technique for intrusion detection of medical devices embedded in a medical cyber physical system (MCPS) to provide security to the patient data.Existing system place some behavior rules in the system then the system monitors the behavior and specify the deviation. To overcome this proposed system focus on advanced medical cyber physical system (AMCPS) which will effectively check the real reports and find the hidden attackers to support ultra-safe and secure MCPS applications.

Reconstructing the damaged images and improving the quality of an image, results in image restoration. Here anisotropic diffusion based iterative inpainting developed to minimise the noise level in the colour images and enhancing the image boundaries, this approach observed on speckle, Gaussian and shot noise. To reduce noise and topological defects from images, 3D- anisotropic diffusion used to decompose the image into high frequencies and low frequencies and protects the image from losing the information, to enhance the image quality, image inpainiting was used. In this process most of the high frequency decomposed sections got damaged with noise and appears as there is information available at those pixels, therefore the complete restoration process was done on all the high frequency decomposed components so this results in achieving better restored images in mean time. The two effects on images can be reduced by the mixed fusion algorithm i.e., noise reduction by using anisotropic diffusion and distance based neighbourhood image inpainting for restoring the damaged parts. So, this results in reconstructing the damaged image and enhancing the boundaries of the image.

Image matching is a quite challenging task to identify matching images in the data. There are multiple methods in computer vision techniques such as histogram-based algorithms, colour or edge based algorithms, textual based features, SIFT and Surf algorithms which will help to identify similar images. Here in our paper we are addressing an industrial problem to provide the better solution where US multinational courier delivery service facing challenges in delivering the products where labels/tags and bar codes of the products are missed while delivering to the customers and customers comes with the product image and with some information about the product. The job is to map the user or customer product information with the existing missed products. The advances in computer science and availability of GPU Machines, the problem will be addressed, and solutions can be automated using deep learning approaches. The paper describes the solution of matching the solution accurately and comparing the solution with the existing classical computer vision algorithms.