Scientific Transactions in Environment and Technovation Journal(STET), 11 Sep 2022 | 10.56343/STET.
Year : 2018 | Volume: 12 | Issue: 2 | Pages : 87-91

Various approaches for Image Denoising and Image inpainting

Image inpainting is used for reconstructing missing parts of an image so that Learners are unable to tell that these regions have undergone restoration. We can remove unwanted objects from an image or to restore damaged portions
of old photos. Image inpainting is an ancient art that originally required human artists to do the work by hand. But today, researchers have proposed numerous automatic inpainting methods. In addition to the image, most of these methods also require as input a mask showing the regions that require inpainting. Here, we compare nine automatic inpainting methods with results from professional artists. Image denoising is an important image processing task, both as a process itself, and as a component in other processes. Very many ways to denoise an image or a set of data exists. The main properties of a good image denoising model are that they will remove noise while preserving edges. Traditionally, linear models have been used. One common approach is to use a Gaussian filter, or equivalently solving the heat-equation with the noisy image as input-data, i.e. a linear, 2nd order PDE-model. For some purposes this kind of denoising is adequate. One big advantage of linear noise removal models is the speed. But a back draw of the linear models is that they are not able to preserve edges in a good manner: edges, which are recognized as discontinuities in
the image, are smeared out. In this study a novel approach to denoising has been used, that is level set approach is employed. Level Set Methods offer an appealing approach to noise removal. In particular, they exploit the fact that curves moving under their curvature smooth out and disappear. 

Keywords: Gaussian denoising, single image super-resolution (SISR) and JPEG image deblocking, DnCNN, AWGN.

Citation: *, ( 2018), Various approaches for Image Denoising and Image inpainting. Scientific Transactions in Environment and Technovation Journal(STET), 12(2): 87-91

Received: 10/18/2017; Accepted: 11/22/2018;
Published: 11/09/2022

Edited by:

Reviewed by:

*Correspondence: R.Revathi