A multi-scale face image deblurring algorithm based on separation of low frequency and high frequency

Abstract

The invention belongs to the technical field of digital image intelligent processing, and particularly relates to a multi-scale face image deblurring algorithm based on separation of low frequency andhigh frequency. The algorithm comprises the steps that a preprocessing network is used, and low-frequency information and semantic segmentation of a face image are recovered at the same time; Then the generated low-frequency information, semantic segmentation and fuzzy picture splicing are input into a subsequent multi-scale network to recover a clear picture; And finally, the deblurring network(a pre-processing network and a multi-scale network) and a high-level task are jointly trained, so that a clear picture generated by deblurring can be better expressed on the recognition of equal-level tasks. Experimental results show that the restored clear picture is very complete in structure and details, and meanwhile, the restored clear picture contains richer perceptual information through combination with a high-level task, so that the effect of the high-level task is greatly improved.

Description

technical field [0001] The invention belongs to the technical field of digital image intelligent processing, and specifically relates to a face image deblurring algorithm, more specifically, relates to a multi-scale face image deblurring algorithm based on separating low and high frequencies. Background technique [0002] In recent years, with the popularity of mobile devices, taking pictures has become an indispensable part of daily life. However, holding mobile devices such as mobile phones will inevitably cause shaking when taking pictures, which will lead to motion blur in the image, especially in low-light scenes, which require a longer exposure time. Motion blur can have a severe impact on high-level tasks. Taking the face image as an example, blurring the face will cause difficulties in face recognition and face key point detection. Face image deblurring, which can deblur the blurred face image and restore the structure and details of the face. It plays an importan...

AI Technical Summary

Problems solved by technology

Although these methods have tried to use multi-scale features, their use of features is not comprehensive enough, they only scale the image, and the feature extraction is not sufficient.

[5] Use the semantic segmentation of the face to assist the deblurring of the face image, but the semantic segmentation information only has part of the structural information of the face, so the help to the face deblurring algorithm is limited

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