Abstract
Abstract. We present a denoising algorithm for the pixel-response non-uniformity correction of a scientific complementary metal–oxide–semiconductor (CMOS) image sensor, which captures images under extremely low-light conditions. By analyzing the integrating sphere experimental data, we present a pixel-by-pixel flat-field denoising algorithm to remove this fixed pattern noise, which occur in low-light conditions and high pixel response readouts. The response of the CMOS image sensor imaging system to the uniform radiance field shows a high level of spatial uniformity after the denoising algorithm has been applied.
Highlights
Complementary metal–oxide–semiconductor (CMOS) image sensors are the most extensively used sensor devices in generic digital cameras because of their high resolution, acceptable linearity, small size, cheap price, rapid response, and durability (Fowler, 2009)
The CIS2521F has high frames per second imaging rates and high sensitivity, which are important for Unmanned Vehicle Systems (UVS) imaging under extremely low-light conditions in remote sensing mapping applications
The Fairchild Imaging CIS2521F is a large-format, low-noise sCMOS image sensor intended for applications requiring highquality imaging under extremely low-light conditions (Fairchild Imaging, 2015)
Summary
Complementary metal–oxide–semiconductor (CMOS) image sensors are the most extensively used sensor devices in generic digital cameras because of their high resolution, acceptable linearity, small size, cheap price, rapid response, and durability (Fowler, 2009). In spite of these characteristics, CMOS image sensors are imperfect detectors when they are used as instruments for generic scientific imaging. For CMOS, obtaining accurate radiometric measurements is difficult and the signal-to-noise ratio (SNR) is always unstable Several noise sources, such as dark current noise, amplifier noise, read noise, and fixed pattern noise, which are inherent in the performance of a camera, alter the digital number (DN) value of each pixel and degrade real image quality (Kawai, 2004; Kim, 2009; Zhang, 2011). We present a pixel-by-pixel flat-field denoising algorithm to remove this fixed pattern noise
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