Systems Biology for Translational Medicine

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Presentation transcript:

Systems Biology for Translational Medicine Prospective Project Partnership Meeting Sparse Representation over Multiple Learned Dictionaries via the Gradient Operator Properties with Application to Single-Image Super-resolution Faezeh Yeganli Faculty of Engineering Department of Computer Engineering

Field of Study / Technical Experience Keywords: Sparse Representation, Image Processing, Biomedical Imaging, Single Image Super-Resolution General Information: Sparse signal representation has received a lot of attention as a successful representation framework in many signal and image processing areas. Sparse representation using over-complete dictionaries has been employed in image denoising, super-resolution, compression, pattern recognition and inpainting over the last decades.

DL Block Diagram

Super-Resolution (SR) Given a LR image, obtaining HR image estimate of the same scene is known as super-resolution (SR).

Proposed Method

Image SR via Sparse Representation over Multiple Learned Dictionaries Based on Edge Sharpness and Gradient Phase Angle The two measures (SM and DPA) are combined together. This is done by first clustering training data based on SM, then using DPA as a secondary classifier to classify data in each SM cluster. X

Training Stage Coupled DL

Upsample to the MR level Reconstruction Stage Upsample to the MR level Divide into patches Extract features

Visual Comparison The Proposed SR Algorithm

Project Idea / Contribution Fields Proposed project title: This work is aimed at improving the quality of sparse representation over a set of compact class dictionaries to increase the performance. List of the fields you may participate and/or techniques you may offer: Image Processing, Super-Resolution, Biomedical Image Processing.

Faculty of Engineering Computer Engineering Department Contact Details Faezeh Yeganli Faculty of Engineering Computer Engineering Department Faezeh.yeganli@ieu.edu.tr