Image super-resolution method based on active sampling and Gaussian process regression

Abstract

The present invention discloses an image super-resolution method based on active sampling and Gaussian process regression, which mainly aims to solve the problem of poor super-resolution effects of a texture region in the prior art. According to the method, a training set is constructed by fully using a large number of existing external natural images, and a sample information amount is measured by using characterization and diversity as indexes, so as to extract a simplified training subset, which enables training of a Gaussian process regression model to be more efficient. The implementation steps are as follows: 1. generating an external training sample set, and performing active sampling on the external training sample set to obtain a training subset; 2. learning a Gaussian process regression model based on the training subset; 3. preprocessing a test image and generating a test sample set; and 5. applying the learned Gaussian process regression model on the test sample set, and predicting and outputting a super-resolution image. The method has a relatively strong super-resolution ability and is capable of restoring more detail information in regions such as texture, so that the method can be used for video monitoring, criminal investigation, aerospace, high definition entertainment and video or image compression.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image super-resolution method, which can be applied to video monitoring, criminal investigation, aerospace, high-definition entertainment, video or image compression. Background technique [0002] Image super-resolution technology aims to estimate the corresponding hidden high-resolution image from one or more low-resolution images of the same scene. This technology can restore more image details and improve recognition, so it has very important use value . [0003] In general, image super-resolution methods can be broadly classified into three categories: interpolation-based methods, reconstruction-based methods, and instance-based learning methods. [0004] Interpolation-based methods use different kernel functions to estimate unknown pixels on a high-resolution image grid with known pixel values. Typical interpolation methods include bicubic interpola...

AI Technical Summary

Problems solved by technology

[0007] Although the above learning-based methods can restore more detailed textures, ringing or noise artifacts will be generated due to incompatible feature matching, which will affect the recognition of details in the final super-resolution reconstruction image and reduce the quality of reconstruction.

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