Deep Learning-based Color Compensation Method for Projected Images in Combat Assistant System

doi:10.3969/j.issn.1000-1093.2021.11.015

Abstract

Abstract: A deep learning-based color compensation method for projected images is proposed to realize the visual display of large-scale data in arbitrary complex color plane.The proposed method is aimed to realize the rapid deployment of display system in complex environment and improve the mobility of field command system in modern war. The method is used to avoid the inherent defects of multiple modules in deep learning based on an end-to-end deep learning network，a model and an objective function. The number of image features extracted from the training model is increased by deepening the layers of CompenNet. The improved loss function SSIM+Smooth L1 is used to calculate image similarity，which enhances the robustness and stability of the loss function while speeding up the convergence of the network. The experimental results show that the PSNR and SSIM average values of the images generated by the improved CompenNet after 1 000 training iterations on the same 24 data sets are increased by 5.54% and 0.14%, respectively，the RMSE average value is decreased by 0.14%, and the subjective perception projection effect of human eyes is also better.

Key words: projectedimage, colorcompensation, deeplearning, combatassistantsystem

CLC Number:

TP391.41

ZHANG Feng, ZHANG Chao, YANG Huamin, WANG Fabin. Deep Learning-based Color Compensation Method for Projected Images in Combat Assistant System[J]. Acta Armamentarii, 2021, 42(11): 2418-2423.

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