基于迭代深度网络的红外图像增强算法

由于现有基于深度网络的图像增强模型直接学习退化图像与清晰图像之间的映射函数,忽略了观测模型保真项的约束,导致恢复的图像存在虚假纹理和细节丢失.本文提出了一种用于红外图像增强的改进深度网络,该网络将深度学习网络嵌入到一个迭代的图像增强任务中,通过图像增强模块和反投影模块交错优化,实现数据一致性约束.本文提出的深度网络不仅...

Infrared image enhancement algorithm based on iteration deep convolution network

Since the existing image enhancement model based on the deep network directly learns the mapping relationship between the degraded image and the clear image,and ignores the constraint of the fidelity term in the observation model,the reconstructed image has false texture and loss of details.This paper proposes an improved deep network for infrared image enhancement,which embeds the denoising network module into an iterative-based enhancement task,and achieves data consistency constraint by interleaving and optimizing the denoising auto-encoder module and the back projection module.Our proposed deep network can not only use the deep feature to describe the priori information,but also use the consistency priori of the observation model.The experimental results show that the proposed algorithm can achieve very competitive reconstruction results in image denoising and deblurring tasks,and can also achieve clear reconstruction results in low-contrast areas such as point object.