Aiming at the problems that existing image deblurring algorithms suffer from diffusion and artifacts when dealing with edge loss and the use of full-frame deblurring in video processing does not meet real-time requirements， an Adaptive DeBlurring Generative Adversarial Network （ADBGAN）algorithm based on active discrimination mechanism was proposed. Firstly， an adaptive fuzzy discrimination mechanism was proposed， and an adaptive fuzzy processing network module was developed to make a priori judgment of fuzziness on the input image. When collecting the input， the blurring degree of the input image was judged in advance， and the input frame which was clear enough was eliminated to improve the running efficiency of the algorithm. Then， the incentive link of the attention mechanism was introduced in the process of fine feature extraction， so that weight normalization was carried out in the forward flow of feature extraction to improve the performance of the network to recover fine-grained features. Finally， the feature pyramid fine feature recovery structure was improved in the generator architecture， and a more lightweight feature fusion process was adopted to improve the running efficiency. In order to verify the effectiveness of the algorithm， detailed comparison experiments were conducted on the open source datasets GoPro and Kohler. Experimental results on GoPro dataset show that the visual fidelity of ADBGAN is 2.1 times that of Scale-Recurrent Network （SRN） algorithm， the Peak Signal-to-Noise Ratio （PSNR） of ADBGAN is improved by 0.762 dB compared with that of SRN algorithm， and ADBGAN has good image information recovery ability； in terms of video processing time，the actual processing time is reduced by 85.9% compared to SRN.The proposed algorithm can generate deblurred images with higher information quality efficiently.