Dynamic scene deblurring and image de-raining based on generative adversarial networks and transfer learning for Internet of vehicle

Abstract

Extracting traffic information from images plays an increasingly significant role in Internet of vehicle. However, due to the high-speed movement and bumps of the vehicle, the image will be blurred during image acquisition. In addition, in rainy days, as a result of the rain attached to the lens, the target will be blocked by rain, and the image will be distorted. These problems have caused great obstacles for extracting key information from transportation images, which will affect the real-time judgment of vehicle control system on road conditions, and further cause decision-making errors of the system and even have a bearing on traffic accidents. In this paper, we propose a motion-blurred restoration and rain removal algorithm for IoV based on generative adversarial network and transfer learning. Dynamic scene deblurring and image de-raining are both among the challenging classical research directions in low-level vision tasks. For both tasks, firstly, instead of using ReLU in a conventional residual block, we designed a residual block containing three 256-channel convolutional layers, and we used the Leaky-ReLU activation function. Secondly, we used generative adversarial networks for the image deblurring task with our Resblocks, as well as the image de-raining task. Thirdly, experimental results on the synthetic blur dataset GOPRO and the real blur dataset RealBlur confirm the effectiveness of our model for image deblurring. Finally, as an image de-raining task based on transfer learning, we can fine-tune the pre-trained model with less training data and show good results on several datasets used for image rain removal.

Intelligent Amphibious Ground-Aerial Vehicles: State of the Art Technology for Future Transportation

“…Over the past few years, GAN was quickly applied into the field of image deblurring with its powerful detail generation skill. Wei et al attempted GAN network for motion deblurring, and extended scene into the rainy deblurring by transfer learning [151]. As the losses in his work, the Wasserstein distance [152] was selected for the discriminator and Perceptual loss [153] as the loss function of generator.…”

Signal processing of Internet of Vehicles based on intelligent interference

“…For the loss function of the autoencoder structure, such as formula (6), the pure data input by the autoencoder network can be expressed as x, the input pure data can be expressed as x * c , and the abstract parameters in the implicit part of the structure can be expressed as h c . Formula (10) can be obtained through the encoder: (7) Similarly, according to its symmetric structure, the reconstructed parameters can be encoded to obtain formula (11):…”

“…IoV can obtain environmental information around the vehicle through the image sensor on the vehicle [7,8]. This information processing technology has greatly improved traffic safety.…”