However, there are two drawbacks: first, regardless of the approximation, performance still might be an issue, and second, the results suffer from a lack of semantic understanding of the scene. It uses a fast, structured randomized search to identify the approximate nearest neighbor patches that will fill in the respective part of the image. The most famous and state of the art approach of this method is the PatchMatch algorithm. Many conventional algorithms then analyze the statistical distribution to fill the resulting gap by finding and using nearest neighbor patches. Common for most inpainting algorithms is that an area of an image is highlighted to be corrected. Image Inpainting has been a viable technique in image processing for quite some time, even before "Artificial Intelligence" was on everyone's lips. Together we are working in the EFRE.NRW funded research project KI Design that targets artificial intelligence (AI) and deep learning-based algorithms for image content analysis and modification, as well as a leveraging tool kit for aesthetic improvements. We are a small consortium consisting of the Bochumer Institute of Technology, a research institute aiming to transfer knowledge from academia into industry, and the company IMG.LY, a team of software engineers and designers developing creative tools like the PhotoEditor SDK and the UBQ engine. But let us start with a quick introduction: who are we and why are we concerned with these kinds of topics? Furthermore, we’ll present some quality optimization steps that we have implemented to improve results addressing the transformation to high-resolution outputs. Today we would like to share experiences that we have gained during the application of deep learning inpainting approaches. A) shows the original image B) the masked (input) image C) the results of the inpainting. Image Inpainting aims to cut out undesired parts of an image and fills up missing information with plausible content of patterns, colors, and textures that match the surrounding.įigure 1: Inpainting example. Wonderful! From the field of deep learning, a technique for image manipulation called Image Inpainting makes it possible. Now, imagine you could erase the distracting object just by highlighting it. Later you discover that a distracting object, like a road sign, is ruining your shot. Without hesitation, you grab your camera and capture the sight. You may know this situation: You are out on a trip when suddenly a unique opportunity for a photograph appears, like a wild animal showing up or sun rays breaking through the rain clouds for a few seconds.
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