Total generalized variation-based infrared image multi-sensor super-resolution reconstruction method

Abstract

The invention discloses a total generalized variation-based infrared image multi-sensor super-resolution reconstruction method. The total generalized variation-based infrared image multi-sensor super-resolution reconstruction method mainly comprises the steps of projecting a low-resolution infrared image into the coordinate space of a high-resolution visible image, obtaining a sparse infrared image and solving a data item weighting coefficient according to the sparse infrared image; performing normalization processing on the sparse infrared image and obtaining a normalization infrared image; solving the marginal information of the high-resolution visible image through a phase equalization algorithm; constructing a data item by the data item weighting coefficient and the normalization infrared image; weighting a TGV regular term improved by a first-order gradient operator through the marginal information of the visible image and constructing a regular bound term; adding the data item and the regular bound term to construct an objective function, solving the objective function in an iterative mode through a primal-dual optimization algorithm with the normalization infrared image serving as an initial value and obtaining a reconstructed high-resolution infrared image. Experiments show that the quality of the image reconstructed by the total generalized variation-based infrared image multi-sensor super-resolution reconstruction method is high and the image is close to an original high-resolution infrared image.

Description

technical field [0001] The invention relates to an infrared image processing technology, in particular to a method for super-resolution reconstruction of a low-resolution infrared image guided by a high-resolution visible light image, and belongs to the technical field of infrared image super-resolution. Background technique [0002] Infrared images reflect the radiation characteristics of the scene, which can provide valuable information for many application fields, such as military reconnaissance and remote control, etc., but compared with visible light images, infrared images have blurred edges and lack texture information. The spatial resolution of infrared images acquired by sensors is limited, so it is difficult to directly extract sufficient information from infrared images. Improving the spatial resolution of infrared images is an urgent problem to be solved. [0003] In order to solve the above-mentioned problems in infrared images, people first researched from the...

AI Technical Summary

Problems solved by technology

The training process of the sample library in this technology has a large computational complexity

The above traditional image super-resolution reconstruction methods have their own shortcomings, and most of them use the images obtained by the same sensor for reconstruction. However, the image information obtained by the same sensor is limited and cannot provide more comprehensive and usable image information. Therefore, based on the traditional The quality of the high-resolution infrared images reconstructed by the method is poor

Images