基于通道校正卷积的真彩色微光图像增强

True Color Low-Light Image Enhancement Based on Channel-Calibrated Convolution

针对现有真彩色夜视相机所成图像亮度低、对比度低、噪声和色彩失真等问题,提出基于通道校正卷积的神经网络算法。通道校正卷积的上分支引入通道注意力块分析RGB通道之间的特征,用来代替U-Net网络中的传统卷积,实现颜色恢复并保留更多图像信息;在传统损失函数中增加Sobel损失函数和色彩损失函数,抑制噪声的同时并保护图像细节,减小色差、增强对比度。采集真实场景下的图像数据集,提升对实际数据的处理效果。实验结果表明:该算法能同时处理低照度图像的亮度、对比度、噪声和色差问题,增强效果优于目前主流算法;与传统卷积的U-Net网络相比,降低了模型复杂度,提高了运行速度,计算量减少了13.71%,参数减少了13.65%,PSNR值提升了29.20%,SSIM值提升了7.23%,色差减少了10.46%,兼顾了成像质量与成像速度。

Aiming at the problems of low brightness,low contrast,noise and color distortion of images produced by existing true color night vision cameras,a neural network algorithm based on channel-calibrated convolution is proposed.The upper branch of the channel-calibrated convolution introduces a channel attention block to analyze the features between the RGB channels.This replaces the traditional convolution in the U-Net network,enabling color recovery and the retention of more image information.The Sobel loss function and color loss function are added to the traditional loss function to suppress noise,preserve image details,reduce chromatic aberration,and enhance contrast.An image dataset under real conditions is collected,which improves the processing effect of the actual data.The experimental results show that the algorithm in this paper can simultaneously deal with the brightness,contrast,noise and chromatic aberration of low-light images,and the enhancement effect is better than the existing mainstream algorithms.Compared with the traditional convolutional U-Net network,the novel method reduces the model complexity and improves operating speed,with a 13.71%reduction in computation,a 13.65%reduction in parameters,a 29.20% increase in PSNR,a 7.23% increase in SSIM,and a 10.46%decrease in chromatic aberration.The algorithm in this paper strikes a balance between imaging quality and speed.