1. 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

2. 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.

3. DL Block Diagram

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

5. Proposed Method

6. 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

7. Training Stage
Coupled DL

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

9. Visual Comparison
The Proposed SR Algorithm

10. 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.

11. Faculty of Engineering Computer Engineering Department
Contact Details
Faezeh Yeganli
Faculty of Engineering
Computer Engineering Department