Feature Enhancement in Visually Impaired Images

TL;DR

This paper introduces the Phase Stretch Transform, a novel computational technique inspired by photonic physics, that enhances feature detection and dynamic range in visually impaired images, outperforming traditional methods especially at low contrast levels.

Contribution

The paper presents the Phase Stretch Transform, a new image analysis method that improves feature detection and dynamic range in visually impaired images, with a reconfigurable operator for hyper-dimensional classification.

Findings

Superior performance at low contrast levels

High dynamic range in visually impaired images

Enhanced image analysis through group delay dispersion

Abstract

One of the major open problems in computer vision is detection of features in visually impaired images. In this paper, we describe a potential solution using Phase Stretch Transform, a new computational approach for image analysis, edge detection and resolution enhancement that is inspired by the physics of the photonic time stretch technique. We mathematically derive the intrinsic nonlinear transfer function and demonstrate how it leads to (1) superior performance at low contrast levels and (2) a reconfigurable operator for hyper-dimensional classification. We prove that the Phase Stretch Transform equalizes the input image brightness across the range of intensities resulting in a high dynamic range in visually impaired images. We also show further improvement in the dynamic range by combining our method with the conventional techniques. Finally, our results show a method for computation of mathematical derivatives via group delay dispersion operations.