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Adaptive Depth Guided Image Completion for Structure and Texture Synthesis

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posted on 22.05.2021, 09:34 by Michael Luigi Ciotta
The problem of synthesis of missing image parts represents an interesting and challenging area of image processing and computer vision with significant potential. This thesis, focuses on an adaptive depth-guided image completion method that addresses the image completion problem using information contained in the rest of the image. The completion process is separated into structure and texture synthesis. A method is first introduced for completing the respective depth map through the use of a diffusion-based operation, preserving global image structure within the unknown region. Building upon the state of the art exemplar based inpainting technique of Barnes et al., we complete the target (unknown) region by matching to and blending source patches drawn from the rest of the image, using the reconstructed depth information to guide the completion process. Secondly, for each target patch, we formulate an adaptive patch size determination as an optimization problem that minimizes an objective function involving local image gradient magnitude and orientations. An extension to the coherence- based objective function introduced by Wexler et al. is then introduced, which not only encourages coherence of the respective target region with respect to the source region in colour but also in depth. We further consider the variance between patches in the SSD criteria for preventing error accumulation and propagation. Experimental results show that our method can provide a significant improvement to patch-based image completion algorithms shown by PSNR and SSIM calculations as well as a qualitative subjective study.





Master of Applied Science


Electrical and Computer Engineering

Granting Institution

Ryerson University

LAC Thesis Type