  In recent years, there has been a gradual increase in the performance of
Complementary Metal Oxide Semiconductor (CMOS) cameras. These cameras have
gained popularity as a viable alternative to charge-coupled device (CCD)
cameras in a wide range of applications. One particular application is the CMOS
camera installed in small space telescopes. However, the limited power and
spatial resources available on satellites present challenges in maintaining
ideal observation conditions, including temperature and radiation environment.
Consequently, images captured by CMOS cameras are susceptible to issues such as
dark current noise and defective pixels. In this paper, we introduce a
data-driven framework for mitigating dark current noise and bad pixels for CMOS
cameras. Our approach involves two key steps: pixel clustering and function
fitting. During pixel clustering step, we identify and group pixels exhibiting
similar dark current noise properties. Subsequently, in the function fitting
step, we formulate functions that capture the relationship between dark current
and temperature, as dictated by the Arrhenius law. Our framework leverages
ground-based test data to establish distinct temperature-dark current relations
for pixels within different clusters. The cluster results could then be
utilized to estimate the dark current noise level and detect bad pixels from
real observational data. To assess the effectiveness of our approach, we have
conducted tests using real observation data obtained from the Yangwang-1
satellite, equipped with a near-ultraviolet telescope and an optical telescope.
The results show a considerable improvement in the detection efficiency of
space-based telescopes.
