To date, pixel noise in a dynamic vision sensor (DVS) has not been accurately analyzed in the literature, and its optimization has been performed empirically. This paper presents a theoretical analysis of the DVS pixel noise. The mean-squared noise voltage at the pixel output from each noise source in a pixel is mathematically derived and verified based on simulations and measurements. A design method to determine the pixel bias currents for a given photocurrent is also presented based on the noise analysis to improve noise performance while maintaining pixel latency. A prototype DVS chip was fabricated in a 110 nm complementary metal-oxide-semiconductor image sensor process and tested under various light and pixel bias conditions. It is shown that the proposed noise analysis and design method successfully predicted the noise performance of the DVS chip.

Computer graphics (CG) rendering platforms produce imagery with ever-increasing photo realism. The narrowing domain gap between real and synthetic imagery makes it possible to use CG images as training data for deep learning models targeting high-level computer vision tasks, such as autonomous driving and semantic segmentation. CG images, however, are currently not suitable for low-level vision tasks targeting RAW sensor images. This is because RAW images are encoded in sensor-specific color spaces and incur pre-white-balance color casts caused by the sensor’s response to scene illumination. CG images are rendered directly to a device-independent perceptual color space without needing white balancing. As a result, it is necessary to apply a mapping procedure to close the domain gap between graphics and RAW images. To this end, we introduce a framework to process graphics images to mimic RAW sensor images accurately. Our approach allows a one-to-many mapping, where a single graphics image can be transformed to match multiple sensors and multiple scene illuminations. In addition, our approach requires only a handful of example RAW-DNG files from the target sensor as parameters for the mapping process. We compare our method to alternative strategies and show that our approach produces more realistic RAW images and provides better results on three low-level vision tasks: RAW denoising, illumination estimation, and neural rendering for night photography. Finally, as part of this work, we provide a dataset of 292 realistic CG images for training low-light imaging models.