Face super-resolution reconstruction system based on combined multi-task learning

Abstract

The invention provides a face super-resolution reconstruction system based on combined multi-task learning. The face super-resolution reconstruction system comprises an acquisition module, a first extraction module, a reconstruction module, a second extraction module and a training module. The method comprises the steps of obtaining shared representation of face features among related tasks through a joint training method for face multi-attribute learning tasks; then, demonstrating the feasibility of the perception loss in the aspect of improving the reconstruction effect of the human face semantic information; and finally, enhancing the face attribute data set, screening out data without related attribute tags, re-extracting the attributes of the feature points by using a face key point detection algorithm, and carrying out combined multi-task learning on the basis to generate a super-resolution result with a more real visual perception effect.

Description

technical field [0001] The invention relates to face restoration technology, which is suitable for reconstruction and restoration of face images at low resolution, and in particular to a face super-resolution reconstruction system based on joint multi-task learning. Background technique [0002] The images collected in the monitoring environment will be affected by the blurring effect of the atmosphere and imaging, as well as the target motion transformation, resulting in the low resolution of the captured face images, which cannot be recognized by humans or machines. Therefore, it is important to improve the clarity of the acquired pictures A problem that needs to be solved. Using face super-resolution restoration technology to enhance the resolution of face images has become an important means to solve this problem. Face super-resolution reconstruction is the process of predicting high-resolution face images from one or more observed low-resolution face images, which is a...

AI Technical Summary

Problems solved by technology

However, due to the instability of the training process of the generative confrontation network, artifacts often appear in the output results

At the same time, due to the uneven quality of face super-resolution data, it is difficult for the model to distinguish the real relevant information from the noise data.

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