提出低比特梯度相机方案，解决高通量视频读取与传输瓶颈。
How to Design a Compact High-Throughput Video Camera?
- 基于梯度相机实现快速读出与高效表征
- 多尺度重建网络恢复高分辨率图像，实测效果佳
- 适合需要高速低延迟视频采集的系统设计
高通量视频采集面临严峻挑战，现有系统通过拼接数百个子图像/视频实现高吞吐，但系统复杂度极高。随着像素尺寸降至亚微米级别，单芯片集成超高通量成为可能，但读出与输出传输速度难以跟上像素数量增长。本文分析梯度相机在快速读出和高效表示方面的优势，提出一种基于现有技术的低比特梯度相机方案，可有效缓解读出与传输瓶颈。同时，设计多尺度重建卷积神经网络以恢复高分辨率图像。在模拟与真实数据上的大量实验验证了该方法在图像质量与可行性方面的优越性。
原文摘要 · Abstract (English)
High throughput video acquisition is a challenging problem and has been drawing increasing attention. Existing high throughput imaging systems splice hundreds of sub-images/videos into high throughput videos, suffering from extremely high system complexity. Alternatively, with pixel sizes reducing to sub-micrometer levels, integrating ultra-high throughput on a single chip is becoming feasible. Nevertheless, the readout and output transmission speed cannot keep pace with the increasing pixel numbers. To this end, this paper analyzes the strength of gradient cameras in fast readout and efficient representation, and proposes a low-bit gradient camera scheme based on existing technologies that can resolve the readout and transmission bottlenecks for high throughput video imaging. A multi-scale reconstruction CNN is proposed to reconstruct high-resolution images. Extensive experiments on both simulated and real data are conducted to demonstrate the promising quality and feasibility of the proposed method.
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