Intelligent transportation system (ITS) plays an important role in assisting drivers to master road information and optimize traffic flow. However, image degradation resulting from complicated environmental factors, such as motion blur caused by vehicle movement and illumination condition, caused some difficulties in current object recognition research on the ITS that may pose serious risks to driving safety. In order to tackle these challenges, this paper introduces a novel perturbation-based image super-resolution method based on GAN inversion (PSRGANI), utilizing a perturbation mechanism to better assist the latent space escape from the local optimum. In the architecture of PSRGANI, a dual encoder that preserves high-dimensional semantic feature from degraded images, extracts texture information from high-resolution (HR) images and completes the SR reconstruction process via perturbation mechanism. The additional encoder effectively decreases illuminated interference from external environment, enhancing the result robustness of SR reconstruction. A dual discriminator precisely regulates the upsampling process of the decoder for improving the quality of generated images. The additional discriminator achieves the optimal fusion of inputs from different sources by decoupling. SR experiment results reveal the higher evaluation metrics of PSRGANI in texture and decoupling compared with other SR models such as ESRGAN and DGP. In real-world ITS of traffic sign experiments, PSRGANI-applied model shows better performance (Top-1 Class Error of 3%) and faster inference speed (0.06 s per image) when compared to target detection algorithms such as YOLO and DETR. PSRGANI is demonstrated to have accurate results on degraded image recognition in terms of texture quality and evaluation metrics.