VEViD: Vision Enhancement via Virtual diffraction and coherent Detection
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Aug 2022.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 1.
2 papers.
- 2023PhyCV: The First Physics-inspired Computer Vision Librarycited by 1
- 2026Standardizing Medical Images at Scale for AIcited by 0
- 2026Standardizing Medical Images at Scale for AI16 Mar 2026
- 2023PhyCV: The First Physics-inspired Computer Vision Library29 Jan 2023