Electrical and Computer Engineering Department, UCLA, Los Angeles, USA
*Bahram Jalalijalali@ucla.edu
纸质出版日期:2022-12,
网络出版日期:2022-11-08,
收稿日期:2022-08-26,
修回日期:2022-09-27,
录用日期:2022-09-29
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VEViD: Vision Enhancement via Virtual diffraction and coherent Detection[J]. 光:快讯(英文), 2022,2(1).
Bahram Jalali, Callen MacPhee. VEViD: Vision Enhancement via Virtual diffraction and coherent Detection[J]. eLight, 2022,2(1).
VEViD: Vision Enhancement via Virtual diffraction and coherent Detection[J]. 光:快讯(英文), 2022,2(1). DOI: 10.1186/s43593-022-00034-y.
Bahram Jalali, Callen MacPhee. VEViD: Vision Enhancement via Virtual diffraction and coherent Detection[J]. eLight, 2022,2(1). DOI: 10.1186/s43593-022-00034-y.
The history of computing started with analog computers consisting of physical devices performing specialized functions such as predicting the position of astronomical bodies and 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 becomes an image enhancement tool. Vision Enhancement via Virtual diffraction and coherent Detection (VEViD) reimagines a digital image as a spatially varying metaphoric "lightfield" 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 dependence on frequency which is different from the monotonically-increasing 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 extremely fast
interpretable
and reduces to a compact and intuitively-appealing mathematical expression. We demonstrate image enhancement of 4k video at over 200 frames per second and show the utility of this physical algorithm in improving the accuracy of object detection in low-light conditions by neural networks. The application of VEViD to color enhancement is also demonstrated.
Physics-inspired computer visionNatural algorithmsLow-light image enhancementColor enhancement
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