通过局部调整增益和像素合并,显著提升图像传感器的信噪比与动态范围。
Towards Spatially-Varying Gain and Binning
- 根据场景亮度动态调节增益,使读噪声可忽略
- 空间自适应像素合并使信噪比提升一个数量级
- 适用于HDR成像、暗角校正等场景,数字合并更优
图像传感器像素尺寸不断缩小以追求更高分辨率,但像素越小积累光量越少,导致画质下降。本文提出空间可变增益与像素合并策略,以改善传感器的噪声表现与动态范围。首先,通过按局部场景亮度变化增益,使读噪声可忽略,动态范围提升一个数量级;其次,提出一种简单分析方法,确定在特定光照条件下最优的像素合并大小,据此设计基于亮度的空间自适应合并策略,有效提高整体信噪比而不牺牲分辨率。我们对比了模拟与数字合并模式,发现当允许更大增益时,数字合并性能优于模拟合并。最后,验证了该方法在高动态范围成像、暗角校正及镜头畸变补偿中的应用效果。
原文摘要 · Abstract (English)
Pixels in image sensors have progressively become smaller, driven by the goal of producing higher-resolution imagery. However, ceteris paribus, a smaller pixel accumulates less light, making image quality worse. This interplay of resolution, noise, and the dynamic range of the sensor and their impact on the eventual quality of acquired imagery is a fundamental concept in photography. In this paper, we propose spatially-varying gain and binning to enhance the noise performance and dynamic range of image sensors. First, we show that by varying gain spatially to local scene brightness, the read noise can be made negligible, and the dynamic range of a sensor is expanded by an order of magnitude. Second, we propose a simple analysis to find a binning size that best balances resolution and noise for a given light level; this analysis predicts a spatially-varying binning strategy, again based on local scene brightness, to effectively increase the overall signal-to-noise ratio. % without sacrificing resolution. We discuss analog and digital binning modes and, perhaps surprisingly, show that digital binning outperforms its analog counterparts when a larger gain is allowed. Finally, we demonstrate that combining spatially-varying gain and binning in various applications, including high dynamic range imaging, vignetting, and lens distortion.
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