In recent years,the rapid development of artificial intelligence has driven the widespread deployment of visual systems in complex environments such as autonomous driving,security surveillance,and medical diagnosis.Ho...In recent years,the rapid development of artificial intelligence has driven the widespread deployment of visual systems in complex environments such as autonomous driving,security surveillance,and medical diagnosis.However,existing image sensors—such as CMOS and CCD devices—intrinsically suffer from the limitation of fixed spectral response.Especially in environments with strong glare,haze,or dust,external spectral conditions often severely mismatch the device's design range,leading to significant degradation in image quality and a sharp drop in target recognition accuracy.While algorithmic post-processing(such as color bias correction or background suppression)can mitigate these issues,algorithm approaches typically introduce computational latency and increased energy consumption,making them unsuitable for edge computing or high-speed scenarios.展开更多
基金supported in part by STI 2030-Major Projects(2022ZD0209200)in part by National Natural Science Foundation of China(62374099)+2 种基金in part by Beijing Natural Science Foundation−Xiaomi Innovation Joint Fund(L233009)Beijing Natural Science Foundation(L248104)in part by Independent Research Program of School of Integrated Circuits,Tsinghua University,in part by Tsinghua University Fuzhou Data Technology Joint Research Institute.
文摘In recent years,the rapid development of artificial intelligence has driven the widespread deployment of visual systems in complex environments such as autonomous driving,security surveillance,and medical diagnosis.However,existing image sensors—such as CMOS and CCD devices—intrinsically suffer from the limitation of fixed spectral response.Especially in environments with strong glare,haze,or dust,external spectral conditions often severely mismatch the device's design range,leading to significant degradation in image quality and a sharp drop in target recognition accuracy.While algorithmic post-processing(such as color bias correction or background suppression)can mitigate these issues,algorithm approaches typically introduce computational latency and increased energy consumption,making them unsuitable for edge computing or high-speed scenarios.
