Light Field Inpainting Propagation via Low Rank Matrix Completion - 2018 PROJECT TITLE :Light Field Inpainting Propagation via Low Rank Matrix Completion - 2018ABSTRACT:Building up on the advances in low rank matrix completion, this Project presents a unique method for propagating the inpainting of the central view of a light field to all or any the other views. Once generating a group of warped versions of the inpainted central read with random homographies, each the initial light field views and also the warped ones are vectorized and concatenated into a matrix. As a result of of the redundancy between the views, the matrix satisfies a coffee rank assumption enabling us to fill the region to inpaint with low rank matrix completion. To the current finish, a replacement matrix completion algorithm, better suited to the inpainting application than existing ways, is additionally developed during this Project. In its straightforward type, our method does not require any depth previous, unlike most existing light field inpainting algorithms. The method has then been extended to higher handle the case where the realm to inpaint contains depth discontinuities. During this case, a segmentation map of the various depth layers of the inpainted central read is needed. This data is employed to warp the depth layers with totally different homographies. Our experiments with natural light fields captured with plenoptic cameras demonstrate the robustness of the low rank approach to noisy knowledge as well as large color and illumination variations between the views of the light field. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images - 2018 Moiré Photo Restoration Using Multiresolution Convolutional Neural Networks - 2018
