Multi-scale neural network infrared image colorization method based on attention mechanism

Abstract

The invention discloses a multi-scale neural network infrared image colorization method based on an attention mechanism, and the method comprises the steps: carrying out the feature extraction of an input infrared image under different resolution scales through a two-dimensional convolutional neural network, and carrying out the extraction of the extracted high-dimensional feature information through the attention mechanism, and finally carrying out fusion processing on the multi-scale information to obtain a predicted colorized infrared image. Compared with an existing infrared image colorization network, the method has the advantages that a neural network algorithm model is constructed based on an attention mechanism and a multi-scale hierarchical structure, and by adopting an improved spatial attention and multi-dimensional feature connection mechanism, the network model feature extraction capability can be improved while the model complexity is effectively reduced; by designing a composite loss function of pixel loss, edge loss and perception loss, the quality of the colorized infrared image is further improved.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to an attention mechanism-based multi-scale neural network infrared image colorization method. Background technique [0002] Infrared image colorization is a research issue that has attracted much attention in the field of computer vision, and has broad application prospects in various systems such as security monitoring, unmanned systems, and military equipment. In particular, how to achieve high-quality, high-resolution, and multi-detail color infrared images is an urgent challenge for this technology. In recent years, artificial intelligence based on deep learning technology has developed rapidly, and breakthroughs have been made in the fields of object detection, image classification, and speech recognition. As one of the application hotspots of computer vision, infrared image colorization also benefits from the continuous innovation of deep neural network t...

AI Technical Summary

Problems solved by technology

However, due to the differences in imaging principles between infrared images and visible light images, directly applying the colorization method of visible light images to infrared image colorization will produce blurred edges and details, resulting in poor imaging quality

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