An image super-resolution algorithm based on context-dependent multi-task depth learning

Abstract

The invention provides an image super-resolution algorithm based on context-related multi-task depth learning, Three depth neural networks are designed for capturing the basic information, the main edge information and the small detail information of the image, and then the neural networks are trained in a multi-task learning framework for context-related connection and unification. Given the input low-resolution image, the trained neural network will output the basic image, the main edge image and the micro-detail image respectively, and the final high-resolution image will be fused from thebasic image and the micro-detail image. The algorithm can recover high resolution (HR) images only from static low resolution (LR) images. Moreover, the structure of the recovered HR image is well preserved, and the structure information in the ideal HR image can be recovered as much as possible.

Description

technical field [0001] The present invention relates to the field of digital image processing, and more specifically, relates to an image super-resolution algorithm based on context-dependent multi-task deep learning. Background technique [0002] Image super-resolution is a technology that improves the resolution of images by computing, and can be widely used in video surveillance and medical image analysis. The problem to be solved by the single image super-resolution algorithm is how to improve the resolution of the image when the input is only a single image. This problem is a fundamental problem in the field of image processing, and it is also a pathological problem. [0003] Traditional solutions can be divided into reconstruction-based algorithms, sample-based methods, and interpolation-based methods. The reconstruction-based method is to model the relationship between the high-resolution (HR) image and the low-resolution (LR) image with a convolution kernel that ac...

AI Technical Summary

Problems solved by technology

Although such methods have greatly improved the quality of image super-resolution, they will also produce some structurally inconsistent defects.

The main reason is that the neural network with the cost function of minimizing the mean square error is difficult to capture the more sensitive structural changes in the human visual system

Recent image super-resolution algorithms try to alleviate this problem by introducing feature-based perceptual error functions. However, although such methods achieve higher visual perception quality on super-resolution images, they also introduce some unreal image details.

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