Due to the low light level and low visibility of night scene, there are many problems in night vision image, such as low signal to noise ratio and low imaging quality. To solve the problems, a dual-channel night vision image restoration method based on deep learning was proposed. Firstly, two Convolutional Neural Network (CNN) based on Fully connected Multi-scale Residual learning Block (FMRB) were used to extract multi-scale features and fuse hierarchical features of infrared night vision images and low-light-level night vision images respectively, so as to obtain the reconstructed infrared image and enhanced low-light-level image. Then, the two processed images were fused by the adaptive weighted averaging algorithm, and the effective information of the more salient one in the two images was highlighted adaptively according to the different scenes. Finally, the night vision restoration images with high resolution and good visual effect were obtained. The reconstructed infrared night vision image obtained by the FMRB based deep learning network had the average values of Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) by 3.56 dB and 0.091 2 higher than the image obtained by Super-Resolution Convolutional Neural Network (SRCNN) reconstruction algorithm respectively, and the enhanced low-light-level night vision image obtained by the FMRB based deep learning network had the average values of PSNR and SSIM by 6.82dB and 0.132 1 higher than the image obtained by Multi-Scale Retinex with Color Restoration (MSRCR). Experimental results show that, by using the proposed method, the resolution of reconstructed image is improved obviously and the brightness of the enhanced image is also improved significantly, and the visual effect of the fusion image obtained by the above two images is better. It can be seen that the proposed algorithm can effectively restore the night vision images.

In order to ensure the correctness of program execution in the safety critical system, the error control theory is used to encode the computer instructions, but the algorithm involves the modular operation, resulting in high additional complexity and difficulty to use in real-time systems. Aiming at reducing the additional complexity, delta code's multiplication and division algorithm was improved. The idea of redundancy encoding and differentiated ideology was introduced to ensure security, while the inverse element was introduced into division to transform division into multiplication, thus avoiding the overhead of the modular operation and reducing the additional complexity while improving the security of the algorithm. Theoretical analysis shows that the undetected error rate is proved to be 2.3*10 ^-10. Simulation results show that the undetected error rate of the proposed algorithm is consistent with the theoretical value, and the complexity is 6.4-7.2 times of the original algorithm, but 7%-19% lower than original delta code. The proposed algorithm satisfies the requirements of safety critical application systems in terms of error detection rate and complexity.

There are the errors that memory data is not updated, the loop exits early and the loop exits late in the program loop control. In order to ensure the correctness of the program execution in the safety critical system, a new error detection algorithm of program loop control based on ANBD-code (arithmetic-code with signature and timestamp) was proposed. Through ANBD-code, the program variables were encoded as a signed code word by the proposed algorithm. And the errors in the loop control were detected by verifying code signature, the error of memory data being not updated could be detected by using the time label of ANBD-code. In addition, on the basis of the ANBD-code, the errors of the loop exiting early and the loop exiting late could be detected by using the online statement block signature allocation algorithm, the block signature function and the variable signature compensation function. The occurrence probability of an undetected error was 1/ A in theory, where A was coding prime. The primes were selected between 97 and 10993 to test occurrence probability of an undetected error and the Normalized Mean Square Error (NMSE) of theoretical model and test result was about-30 dB. The test results show that the proposed algorithm can effectively detect all kinds of errors in the loop control and the occurrence probability of an undetected error is up to 10 ^-9 when the prime A is close to 2 ³². The proposed algorithm can satisfy the requirements of safety critical system.