Elimination of Signal Distortion Using Generative Adversarial Network

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Shabani, Ahmad | 2021

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 54028 (05)
University: Sharif University of Technology
Department: Electrical Engineering
Advisor(s): Bagheri Shouraki, Saeed; Pour Mohammad Namvar, Mehrzad
Abstract:
Nowadays millions of images are shared on social media every day , So image inpainting has become an important issue . After advent of Generative adversarial network image inpainting methodes based on deep learning has been revived and significant progress has been made . For a proper image inpainting , The inpainted image must benefit from the appropriate structure and texture in the missing regions . Therefore, in this project , an attempt is made to use a two-stage structure by using Generative adversarial network .in first stage first by using Gabor filters , the image structure is extracted and then the image structure is completed , while the second stage focuses only on the image textures . We have shown that this strategy will improve the quality of the inpainted image so that Gabor filters can be used as an alternative to extract image structure in two-stage networks based on structure and texture separation
Keywords:
Deep Learning ; Gabor Filters ; Image Inpainting ; Generative Adversarial Networks ; Image Reconstruction

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