International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 3 Issue: 3 931 - 933 _______________________________________________________________________________________________ 931 IJRITCC | March 2015, Available @ http://www.ijritcc.org _______________________________________________________________________________________ Image Mapping and Object Removal Using ADM in Image Inpainting: Review B.H.Deokate 1 , Dr.P.M.Patil 2 , P. B. Lonkar 3 Electronics and Telecommunication Savtribai Phule Pune University, Maharashtra b_ash11@rediffmail.com patil_pm@rediffmail.com poonam.lonkar209@gmail.com Abstract :- Image inpainting is a technology for restoring the damaged parts of an image by referring to the information from the undamaged parts to make the restored image look “complete”, “continuous” and “natural”. Inpainting tradition ally has been done by professional restorers. For instance, in the valuable painting such as in the museum world would be carried out by a skilled art conservator or art restorer. But this process is manual so it is time consuming. Digital Image Inpainting tries to imitate this process and perform the Inpainting automatically. The aim of this work is to develop an automatic system that can remove unwanted objects from the image and restore the image in undetectable way. Among various image inpainting algorithms Alternating Direction Method (ADM) is used for image restoration. ADM works well for solving inverse problem. In this paper, various applications of ADM method for image restoration are discussed. Keywords:- Image inpainting, Alternating Direction Method. __________________________________________________*****_________________________________________________ I. INTRODUCTION Inpainting is the art of restoring lost parts of an image and reconstructing them based on the previous information. The effect of removing object should not be noticeable. The term Inpainting is derived from the ancient art of restoring image by professional image restorers in museums etc. The filling-in of lost information is essential in image processing, with applications including image coding and wireless image transmission (e.g., recovering lost blocks), special effects (e.g., removal of objects), and image restoration (e.g., scratch removal). The basic idea at the back of the algorithms that have been proposed in the literature is to fill-in these regions with available information from their environment. For visual inspection, large areas with lots of information lost are harder to construct again, because information in other parts of the image is not enough to get an knowledge of what is missing. When we take a snapshot, there may be some unwanted object that comes in between. There is a need of system that can efficiently remove the marked object from the image. Details that are completely hidden /occluded by the object to be removed are to be filled in visually plausible way using the background information. Various methods are available for Image inpainting as Structural inpainting, Texture synthesis based Image Inpainting, Partial Differential Equation (PDE) Fig 1: Process of Image Inpainting. based algorithm, Exemplar based Image Inpainting, Wavelet Transform based, Semi-automatic and Fast Inpainting, Alternating Direction Method (ADM) based inpainting. II. RELATED WORK Image restoration is an ill-posed problem, according to [2] Blind image deblurring (BID) is an ill-posed inverse problem, typically solved by imposing some form of regularization (prior knowledge)on the unknown blur and original image. This approach, although not requiring prior knowledge on the blurring filter, achieves state-of-the-art performance for a wide range of real-world BID problems [2]. In this paper a new version of method is proposed in which both the optimization problems with respect to the unknown image and with respect to the unknown blur are solved by the alternating direction method of multipliers (ADMM) .ADMM provides optimization tool that has recently sparked much interest for solving inverse problems. Blind image deblurring (BID) is an inverse problem where the observed image is modeled as resulting from the convolution with a blurring filter and possibly followed by additive noise. The goal is to estimate both the underlying image and the blurring filter. Furthermore, the convolution operator is itself typically ill-conditioned, making the inverse problem extremely sensitive to inaccurate filter estimates and to the presence of noise [2]. In this paper, an improvement is done on method [3].Without increase in computational cost ADMM is used to solve the minimizations required by method [3]. Image estimate obtained by gradient descent and filter estimate by conjugate gradient are calculated then such two steps can be efficiently calculated by ADMM method. Experimental result shows that both results give best ISNR (Improvement in signal to noise ratio). Instead of