Besides, our algorithm can be easily extended to handle practical applications including rendering acceleration, photo restoration and object removal. Experiments show that our algorithm has superior advantages over existing inpainting techniques. For the first problem, we propose a robust patch matching approach, and for the second task, the alternating direction method of multipliers is employed. In our algorithm, how to accurately perform patch matching process and solve the low-rank matrix completion problem are key points. In our framework, we first match and group similar patches in the input image, and then convert the problem of estimating missing values for the stack of matched patches to the problem of low-rank matrix completion, and finally obtain the result by synthesizing all the restored patches. It is an important problem in computer vision and an essential functionality in. Our algorithm is inspired by the recent progress of non-local image processing techniques following the idea of ‘grouping and collaborative filtering’. Image Inpainting is a task of reconstructing missing regions in an image. Patch based methods produces high-quality effects maintaining consistency of local structures.This paper is based on a survey in the area of video inpainting.In this paper, we propose a highly accurate inpainting algorithm which reconstructs an image from a fraction of its pixels. The texture synthesis based methods doesn’t contain structural information while, PDE-based methods leads to blurring artifacts. Hence, the researchers have extended the similar concept in video inpainting. Patch-based methods use block-based sampling as well as simultaneous propagation of texture and structure information as a result of which, computational efficiency is achieved. The methods can be classified as: Patch-based methods and object-based methods. Although the amount of work proposed in video completion is comparatively less as that of image inpainting, a number of methods have been proposed in the recent years. But none of them try to ensure both of them in the same technique with a good quality. Delete any unwanted object from your photo, such as extra power-line, people, and text. Most of the techniques try to ensure either spatial consistency or temporal continuity between the frames. Inpaint may be used unwanted objects from your photos. A lot of researchers have worked in the area of video inpainting. The key issues in video completion are to keep the spatial-temporal coherence, and the faithful inference of pixels. Its goal is to recover images with limited data loss and tries to obtain outputs of damaged area. The problem of video completion whose goal is to reconstruct the missing pixels in the holes created by damage to the video or removal of selected objects is critical to many applications, such as video repairing, movie post production, etc. image from the nearby pixels information by some algorithm. This paper includes various methods for detection of fence(s), various methods for filling the gaps, literature survey and performance analysis of methods for background reconstruction. Also it involves filling the gaps of removed, damaged region to recover lost image details. Afterward, it uses a boundary restoration. The main aim is when a colored image is input having fence in the image and then deleting removing the fence gives the resultant image with the removal of fence from the image. A combined texture synthesis technique in (Wang 2011) also divides the image into a cartoon image and a texture image. Multi-focus images are obtained and “defocusing” information is utilized to generate a clear image. For the background occluded by fences, the goal of image de-fencing is to restore them and return fence-free images. Remove unwanted elements from your photos with greater ease than you thought was possible. Images or videos taken at open places using lowresolution cameras, like smart phones are also frequently corrupted by the presence of occlusions like fences. Photo retouch and restoration made simple with Inpaint. Many scenes such as parks, gardens, and zoos are secured by fences and people can only take pictures through the fences. When a picture is taken, it may have certain structures or objects that are unwanted. In recent world, detection and removal of fences from digital images become necessary when an important part of the view changes to be occluded by unnecessary structures.
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