Changes of factors such as dust

smoke and light inside air preheater cause unclear monitoring video images

which affects the monitoring effect. To solve this problem

this paper proposes a method for sharpening infrared compensation image of monitoring video inside the air preheater based on conditional generative adversarial network(cGAN). In this method

the acquired infrared compensation images are preprocessed

including Gaussian filter de-noising and image splicing

then the composite graphics of low resolution image and high resolution image are obtained. The low resolution image serves as the one to be reconstructed and the high resolution image as the ideal reference one for reconstructing. The cGAN model is established to reconstruct the low-resolution images

and the optimized parameters are adjusted to generate the corresponding high-resolution images. Moreover

two sets of on-site monitoring videos inside the air preheater are adopted in the experiments as the training sets for offline training

and clear processing of infrared compensation images is realized. The method in this paper has small model

simple training process

high computational efficiency and good image clarity processing effect

which is suitable for restoration and clarification of the monitoring image in similar complex engineering scenarios.