Loading...
Search for:
image-decomposition
0.009 seconds
Sparse Representation Based Image Inpainting
, M.Sc. Thesis Sharif University of Technology ; Babaie Zadeh, Massoud (Supervisor)
Abstract
Sparse signal processing (SSP), as a powerful tool and an efficient alternative to traditional complete transforms, has become a focus of attention during the last decade. In this approach, we want to approximate a given signal as a linear combination of as few as possible basis signals. Each basis signal is called an atom and their collection is called a dictionary. This problem is in general difficult and belongs to the Np-hard problems; since it requires a combinatorial search. In recent years however, it has been shown both theoretically and experimentally that the sparset possible representation of a signal in an overcomplete dictionary is unique under some conditions and can be found...
Structure and texture image inpainting
, Article Proceedings of the 2010 International Conference on Signal and Image Processing, ICSIP 2010, 15 December 2010 through 17 December 2010 ; 2010 , Pages 119-124 ; 9781424485949 (ISBN) ; Jamzad, M ; Mahdavi Amiri, N ; Sharif University of Technology
Abstract
Inpainting refers to the task of filling in the missing or damaged regions of an image in an undetectable manner. We have an image to be reconstructed in a user-defined region. We use a fast decomposition method to obtain two components of the image, namely structure and texture. Reconstruction of each component is performed separately. The missing information in the structure component is reconstructed using a structure inpainting algorithm, while the texture component is repaired by a texture synthesis technique. To obtain the inpainted image, the two reconstructed components are composed together. Taking advantage of both the structure inpainting methods and texture synthesis techniques,...
Fast and robust LRSD-Based sar/isar imaging and decomposition
, Article IEEE Transactions on Geoscience and Remote Sensing ; Volume 60 , 2022 ; 01962892 (ISSN) ; Moradikia, M ; Bastami, H ; Abdelhadi, A ; Soltanalian, M ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2022
Abstract
The earlier works in the context of low-rank-sparse-decomposition (LRSD)-driven stationary synthetic aperture radar (SAR) imaging have shown significant improvement in the reconstruction-decomposition process. Neither of the proposed frameworks, however, can achieve satisfactory performance when facing a platform residual phase error (PRPE) arising from the instability of airborne platforms. More importantly, in spite of the significance of real-time processing requirements in remote sensing applications, these prior works have only focused on enhancing the quality of the formed image, not reducing the computational burden. To address these two concerns, this article presents a fast and...