Existing wireless federated learning frameworks lack the effective support for the actual distributed high-speed Internet of Vehicles (IoV) scenes. Aiming at the distributed learning problem in such scenes, a distributed training algorithm based on the random network topology model named Clustered-Wireless Federated Learning Algorithm (C-WFLA) was proposed. In this algorithm, firstly, a network model was designed on the basis of the distribution situation of vehicles in the highway scene. Secondly, the path fading, Rayleigh fading and other factors during the uplink data transmission of the users were considered. Finally, a wireless federated learning method based on clustered training was designed. The proposed algorithm was used to train and test the handwriting recognition model. The simulation results show that under the situations of good channel state and little user transmit power limit, the loss functions of traditional wireless federated learning algorithm and C-WFLA can converge to similar values under the same training condition, but C-WFLA converges faster; under the situations of poor channel state and much user transmit power limit, C-WFLA can reduce the convergence value of loss function by 10% to 50% compared with the traditional centralized algorithm. It can be seen that C-WFLA is more helpful for model training in high-speed IoV scenes.

Vignetting is the phenomenon that the intensity of the pixel in the image decreases along the radial direction. In order to solve the problem that it affects the accuracy of computer vision task and image processing, a method of single image vignetting correction based on constrained log-intensity entropy under low-pass filtering was proposed. Firstly, the vignetting model was established by using a sixth order polynomial function of even term. Secondly, the minimum log-intensity entropy of the target image was calculated by low-pass filtering. Under the constraint of the target value, the optimal parameter solution of the vignetting model was obtained, which can satisfy the change rule of the vignetting function and reduce the log-intensity entropy of the image. Finally, vignetting was eliminated by using inverse compensation of vignetting model. Vignetting correction results were evaluated by Structural SIMilarity index (SSIM) and Root Mean Square Error (RMSE). Experimental results show that the proposed method can not only effectively recover the brightness information of the vignetting area to get real and natural non-vignetting image, but also effectively correct the different degrees of vignetting with a good visual consistency.