基于神经网络的光照分布预测夜视复原算法

Abstract

Abstract: The illumination of the nighttime image is uneven,the overall brightness is low,the color deviation is large,and there is halo near the artificial light source.Existing deblurring models and algorithms often remove the effects of uneven illumination by estimating the illumination map in the case of uneven illumination.By combining the deep learning method with the radial basis function neural network,the illumination intensity was extracted,and the night image deblurring algorithm based on illumination estimation was proposed.For the problem of uneven illumination,the modulation transfer function (MTF) in the imaging process is calculated by estimating the illumination map.Taking the point diffusion function of the transport degrada-tion model as prior knowledge,combining the mathematical model of semi-blind image restoration method,the target image is processed to improve the quality of night vision imaging.In addition,the effectiveness of this method is verified by comparing with the traditional blind restoration method,and the image quality is improved evidently.

Key words: Lighting prediction, Modulation transfer function, Night vision image restoration, Radial basis function neural network, Semi-blind image restoration

CLC Number:

TP183

ZOU Peng, CHEN Yu-zhang, CHEN Long-biao, ZENG Zhang-fan. Night Vision Restoration Algorithm Based on Neural Network for Illumination Distribution Prediction[J].Computer Science, 2019, 46(11A): 329-333.

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Night Vision Restoration Algorithm Based on Neural Network for Illumination Distribution Prediction

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