The Ross equation principle of an ideal detector refers to a mathematical model used to describe the performance of an optical polarization imaging system, which was proposed by American physicist Robert Ross in 19691. The Ross equation can be used to calculate the signal-to-noise ratio, resolution, contrast and other parameters of the polarization imaging system, so as to evaluate the detection ability and image quality of the system.

 

The basic form of the Ross equation is:

 

Among them, SNR represents the signal-to-noise ratio, N represents the number of pixels in the image, P represents the polarization degree of the target, I represents the radiation intensity of the target, B represents the bandwidth of the system, η represents the quantum efficiency of the detector, hν represents the energy of the photon, k represents the Boltzmann constant, T represents the temperature of the system, F represents the noise figure of the system, and R represents the reflectivity of the detector.

 

The Ross equation shows that the signal-to-noise ratio is proportional to the polarization degree of the target, radiation intensity, quantum efficiency of the detector, bandwidth and other factors, and inversely proportional to the number of pixels in the image, system temperature, noise coefficient, reflectivity and other factors. Therefore, in order to improve the performance of the polarization imaging system, the following measures can be taken:

 

(1) Optimize system design. Optimize the five parts from the selection of light source, the transmission of polarized light, the modulation of polarized light, the acquisition of polarized light and the processing of polarized light to reduce the errors caused by each link;

 

(2) Improve the integration process of polarization devices and detectors. Whether it is a micro-polarization array based on a metal wire grid or a polarization device based on a metasurface structure, the high-precision integration process can significantly reduce the crosstalk between pixels and improve the extinction ratio and transmittance;

 

(3) Develop new algorithms and software. Use advanced image processing and machine learning technology to perform denoising, enhancement, segmentation, classification and other operations on polarization images to extract more useful information and features.

 

 

This discovery makes controller design under low light refer to the design of a controller that can effectively detect and process weak signals under weak light conditions using control theory and circuit technology.

 

The controller design in low light has a wide range of applications, such as night vision, astronomical observation, medical imaging, biometrics, etc.