Super-resolution reconstruction method based on conditional generative adversarial network

Abstract

The invention discloses a super-resolution reconstruction method based on a conditional generative adversarial network, and the method specifically comprises the steps: making a low-resolution image and a corresponding high-resolution image training set by using a disclosed super-resolution image data set; constructing a conditional generative adversarial network model, using dense residual blocksin the generator network, and realizing super-resolution image reconstruction at the tail end of the generation network model by using a sub-pixel up-sampling method; inputting the training image setinto a conditional generative adversarial network for model training, and enabling a training model to converge through a perception loss function; carrying out down-sampling processing on the imagetest set to obtain a low-resolution test image; and inputting the low-resolution test image into the conditional adversarial network model to obtain a high-quality high-resolution image. The method can well solve the problems that a super-resolution image generated by a traditional generative adversarial network looks like clear, and evaluation indexes are extremely low, and meanwhile, the problems of gradient disappearance and high-frequency information loss are relieved through a dense residual network.

Description

technical field [0001] The invention relates to a super-resolution reconstruction method, in particular to a conditional generative confrontation network-based super-resolution reconstruction method, and belongs to the technical field of image processing. Background technique [0002] Single image super-resolution (SISR) aims to recover a high-resolution image (HR) from a single low-resolution image (LR). This has direct applications in many fields such as HDTV, medical imaging, satellite imaging, face recognition and video surveillance. At present, people have higher and higher requirements for images, especially in terms of clarity. Improving image clarity purely from the hardware aspect is not only costly but also technically reaches a certain bottleneck. Improve the image resolution from the software, to a certain extent overcome the problem of insufficient hardware. Therefore, image super-resolution reconstruction has become one of the research hotspots. [0003] Th...

AI Technical Summary

Problems solved by technology

Therefore, it may not be optimal to directly apply the residual block architecture to low-level vision problems such as super-resolution

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