## 3 Dark Current Noise Correction Experiment for the LWIR QWIP System
### Dark Current Noise Correction Experiment  
The feature data acquisition area was defined as 126 well-performing dark pixels; 16 imaging pixels which are located in the center, edge, and other areas, were selected as correction target pixels. Figure 8 shows the location of the selected pixels. The data acquisition experiment device is shown in Figure 9a; the blackbody temperature is being adjusted to maintain 298 K. The detection system is oriented to the effective area of the blackbody. In the experiment, the system was powered on and continuously acquired 3000 frames of imaging data; each dataset's number of frames (\(N_{\rm frame}\)) was set to five. A total of 500 datasets were generated in training, followed by 100 datasets for the experiment. As shown in Figure 9b, the feature data of dark pixels and imaging pixels (186, 53) correlated; the overall dark current also changed when there was a slight focal plane temperature change in a specific moment, leading to fluctuations in response values. It is not possible to obtain the dark current response change value of a single imaging pixel when using target-oriented imaging. Figure 7: Radiometric calibration curves of imaging pixels and dark pixels. The RNN algorithm was used to train the feature data of dark pixels and imaging pixels to obtain the mapping relationship between them. The prediction model's evaluation indices include the average absolute error, root mean square error, and average prediction accuracy. The correction effect of the imaging pixels (186, 53) was assessed. The conventional evaluation metrics of prediction models include the following three parameters: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Prediction Accuracy (MPA). MAE is the predicted value minus the true value, and the mean value after finding the absolute value. RMSE is the mean squared error squared. MPA is the mean true value minus the mean error, divided by the mean true value. After calculation, MAE was 1.93, RMSE was 2.28, and MPA was 99.93%. This is sufficient to demonstrate the RNN's excellent performance in the prediction of this model. A comparison of the model's predicted and actual experimental values is shown in Figure 10. Horizontal coordinates indicate the serial number of the experiment set; the left vertical coordinate, Response, indicates the dark level correction value, \(g_{\text{RNN\_img}}(i,j,k)\); Predict output represents the predicted value; Real output represents the actual value; and the right vertical coordinate, Error, indicates the error value. Figure 8: Position of the selected pixels. Figure 9: Characteristic data collection experiment: (**a**) device for collecting characteristic data; (**b**) characteristic data of dark pixels and imaging pixels. In the experiment, 100 frames of target data were acquired. After inputting \(g_{\text{RNN\_dark}}(k)\) into the model, \(g_{\text{RNN\_img}}(i,j,k)\) was obtained and corrected using Equation (8). The responses before and after the correction of the imaging pixels (186, 53) are shown in Figure 11, which shows that the response's fluctuation was reduced after correction. The horizontal axis indicates frame numbers \(F\). The noise reduction effect of this method was examined by comparing the temporal noise before and after the correction of imaging pixels [40]. The noise values before and after correction are calculated as:  
\[g_{\text{noise}}(i,j)=\sqrt{\frac{1}{F-1}\sum_{k=1}^{F}\Bigl{[}g_{\text{pixel }}(i,j,k)-\overline{g}_{\text{pixel}}(i,j)\Bigr{]}^{2}} \tag{9}\]  
\[\overline{g}_{\text{pixel}}(i,j)=\frac{1}{F}\sum_{k=1}^{F}g_{\text{pixel}}(i,j,k) \tag{10}\]  
Figure 11: Response before and after correction of imaging pixels. Figure 10: Comparison of predicted and actual dark level correction values. In this experiment, \(F\) was 100; \(\overline{g}_{\text{pixel}}(i,j)\) is the multi-frame mean response of the imaging pixel.