Single-frame image super-resolution reconstruction method based on cascade regression base learning

Abstract

The invention discloses a single-frame image super-resolution reconstruction method based on cascade regression base learning. The method comprises the following steps: taking a super-resolution reconstruction technology of a single-frame low-resolution image as a research object, learning a multi-layer over-complete sub-dictionary for representing an image structure, constructing a mapping relation between a low-resolution image and a high-resolution image, and learning an optimized regression base and a corresponding coding coefficient; and then, complete super-resolution reconstruction is realized for the low-resolution image set, and the reconstructed image is used as a low-resolution image of the next layer for feature extraction. The invention discloses a single-frame image super-resolution reconstruction method. learning by utilizing a meta-dictionary learning method to obtain a low-resolution dictionary; a weighted linear regression method is used for carrying out multilayer regression base learning on a reconstructed high-resolution training set image and an original high-resolution image in a cascading mode so as to approach a complex nonlinear mapping relation between alow-resolution image and a high-resolution image, and instance regression super-resolution reconstruction with high processing speed, small memory occupation and high reconstruction quality is achieved.

Description

technical field [0001] The invention belongs to the technical field of image processing methods, and in particular relates to a single-frame image super-resolution reconstruction method based on cascade regression basis learning. Background technique [0002] In practical applications, the imaging system is limited by many factors such as device cost, transmission bandwidth, computing resources, and imaging environment. The resolution of the obtained images is often not high, which brings great challenges to subsequent image processing, analysis, and understanding tasks. How to obtain high-resolution digital images is a topic of great concern to people. Undoubtedly, improving the physical resolution of the imaging system is the most direct and effective means to obtain high-resolution images. However, this method is limited by manufacturing technology and device cost, and is limited to some special applications, which is not easy to promote in practical applications; moreov...

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Problems solved by technology

[0007] The purpose of the present invention is to provide a single-frame image super-resolution reconstruction method based on cascaded regression basis learning, which solves the problems of high computational time complexity and space complexity, slow speed, strong dependence on dictionaries, and low reconstruction quality in existing methods The problem

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