Elliptical Shape Recovery from Blurred Pixels Using Deep Learning
Hojatollah Zamani, Peyman Rostami, Arash Amini, Farokh Marvasti
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 2D 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.
BibTeX
@inproceedings{icassp2021_ellipticalshaper,
title = {Elliptical Shape Recovery from Blurred Pixels Using Deep Learning},
author = {Hojatollah Zamani and Peyman Rostami and Arash Amini and Farokh Marvasti},
booktitle = {ICASSP 2021},
year = {2021}
}