VEViD: Vision Enhancement via Virtual diffraction and coherent Detection

TL;DR

VEViD introduces a novel computational imaging paradigm that uses virtual diffraction and coherent detection algorithms to enhance images, especially in low-light and color scenarios, with high efficiency and real-time performance.

Contribution

This work presents a new algorithmic approach to image enhancement inspired by physical diffraction and detection, enabling real-time, high-quality vision improvements without retraining neural networks.

Findings

Achieves 4K video enhancement at 200 fps.

Improves object detection accuracy in low-light conditions.

Enhances color and low-light image quality effectively.

Abstract

The history of computing started with analog computers consisting of physical devices performing specialized functions such as predicting the trajectory of cannon balls. In modern times, this idea has been extended, for example, to ultrafast nonlinear optics serving as a surrogate analog computer to probe the behavior of complex phenomena such as rogue waves. Here we discuss a new paradigm where physical phenomena coded as an algorithm perform computational imaging tasks. Specifically, diffraction followed by coherent detection, not in its analog realization but when coded as an algorithm, becomes an image enhancement tool. Vision Enhancement via Virtual diffraction and coherent Detection (VEViD) introduced here reimagines a digital image as a spatially varying metaphoric light field and then subjects the field to the physical processes akin to diffraction and coherent detection. The term "Virtual" captures the deviation from the physical world. The light field is pixelated and the propagation imparts a phase with an arbitrary dependence on frequency which can be different from the quadratic behavior of physical diffraction. Temporal frequencies exist in three bands corresponding to the RGB color channels of a digital image. The phase of the output, not the intensity, represents the output image. VEViD is a high-performance low-light-level and color enhancement tool that emerges from this paradigm. The algorithm is interpretable and computationally efficient. We demonstrate image enhancement of 4k video at 200frames per second and show the utility of this physical algorithm in improving the accuracy of object detection by neural networks without having to retrain model for low-light conditions. The application of VEViD to color enhancement is also demonstrated.