Single image defogging model based on Convolutional Neural Network （CNN） was difficult to deploy on mobile/embedded system and used for real-time video defogging. To solve this problem， a method of hardware reconstruction and acceleration was proposed， based on Zynq System-on-Chip （SoC）. First， a quantization-dequantization algorithm was proposed to perform quantization on two representative defogging models； second， a quantized defogging model was reconstructed and a hardware IP core with Advanced eXtensible Interface 4 （AXI4） was generated， based on video stream memory architecture， hardware/software co-design， pipeline technology and High-Level Synthesis （HLS） tool. Experimental results show that the model parameters can be quantified from float32 to int5（5 bit） under premise of defogging performance， saving about 84.4% of storage space； the highest pixel clock frequency of the generated hardware IP core is 182 Mpixel/s， which can achieve 1080P@60 frame/s video defogging； the hardware IP core processes a single hazy image with the resolution of 640 pixel × 480 pixel only in 2.4 ms， and the on-chip power consumption is only 2.25 W. This hardware IP core with AXI4 is also convenient for cross-platform migration and deployment， which can expand application scope of CNN-based single image defogging model.