2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| Paper ID | MLSP-2.5 | ||
| Paper Title | Elliptical Shape Recovery from Blurred Pixels using Deep Learning | ||
| Authors | Hojatollah Zamani, Peyman Rostami, Arash Amini, Farokh Marvasti, Sharif University of Technology, Iran | ||
| Session | MLSP-2: Deep Learning Training Methods 2 | ||
| Location | Gather.Town | ||
| Session Time: | Tuesday, 08 June, 13:00 - 13:45 | ||
| Presentation Time: | Tuesday, 08 June, 13:00 - 13:45 | ||
| Presentation | Poster | ||
| Topic | Machine Learning for Signal Processing: [MLR-DEEP] Deep learning techniques | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | In this paper, we study the problem of ellipse recovery from blurred shape images. A shape image is a continuous-domain black and white (binary-valued) image in which the points of the same color form a shape. We assume to have a digitized version of the shape image which is a sampled and blurred version of the image using a $2$D kernel (the point spread function); the resulting pixels may also be corrupted by additive noise. Our goal in this work is to recover the original continuous-domain image based on the available pixels when the shape image is an ellipse. Our approach is to represent an ellipse as the zero-level-set of a bivariate polynomial of degree $2$ and estimate the involved $6$ polynomial coefficients based on a deep neural network. Our model is trained end to end on a wide range of blurring setups with varying noise levels. Besides, the network is trained to recover the ellipse even when the available noisy pixels cover only a part of the ellipse. Simulation results validate the performance of the proposed method and indicate its superiority compared to the state of art methods. | ||