视觉传达下的低分辨率激光图像高分辨率重建方法

High resolution reconstruction method for low resolution laser images under visual communication

低分辨率激光图像处理过程中,运用基于稀疏表示的方法实现图像高分辨率重建,其获取的特征信息模糊,使得模糊特征的峰值信噪比较高,导致图像重建效果差。因此,提出视觉传达下的低分辨率激光图像高分辨率重建方法。运用视觉传达技术下的灵敏度差分算法,处理低分辨率激光图像,使得激光图像包含的特征信息具有较高实质效果。利用四叉树分块思想划分预处理后的激光图像,形成多个图像子区域。从每个子区域图像入手,将其输入光滑主成分分析网络(Smooth-PCANet)中进行深度学习,提取图像的深层次细节特征。以稀疏正则模型为基础,结合非局部近似性先验约束,完成激光图像高分辨率重建。实验结果表明:运用新研究方法对不同类型的激光图像进行处理,重建后图像的峰值信噪比总是超过46 dB,并且结构相似性在0.96以上,实现了图像分辨率的大幅提升。

In the process of low resolution laser image processing,a sparse representation based method is used to achieve high-resolution image reconstruction.The obtained feature information is blurred,the peak signal-to-noise ratio of fuzzy features is high,resulting in poor image reconstruction effect.Therefore,a high-resolution reconstruction method for low resolution laser images under visual communication is proposed.Using sensitivity difference algorithm under visual communication technology to process low resolution laser images,the feature information contained in laser images has a high substantive effect.Using the quadtree partitioning idea to partition the preprocessed laser image,forming multiple image sub regions.Starting from each sub region image,input it into the Smooth PCANet for deep learning to extract deep level detail features of the image.Based on the sparse regularization model and combined with non local approximation prior constraints,high-resolution reconstruction of laser images is achieved.The experimental results show that using new research methods to process different types of laser images,the peak signal-to-noise ratio of the reconstructed images always exceeds 46 dB,and the structural similarity is above 0.96,achieving a significant improvement in image resolution.