基于物联网技术的小波域分块压缩感知算法的图像重构系统设计

Design of Image Reconstruction System for Wavelet Domain Sub-block Compression-aware Algorithm Based on Internet of Things Technology

针对低照明度重构图像分辨率不高、重构时间长的问题,提出了基于小波域分块压缩感知算法的图像重构系统。建立低照明度图像采样模型,采用图像的景深自适应调节方法进行小波域分块压缩感知和信息融合处理。利用多尺度的Retinex算法进行小波域分块压缩感知和信息提取,提取图像的信息熵特征量。采取图像自适应增强方法进行低照度图像增强处理,使用物联网技术进行低照明度图像的三维信息重构,结合细节增强方法进行低照度图像增强处理,完成重构系统设计,实现透射率图的轮廓检测和特征重构。仿真结果表明,采用该方法进行低照明度图像重构的分辨率较高,边缘感知能力较好,且重构耗时较短,实际应用效率较高。

Aiming at the problems of low resolution and long reconstruction time for low-illumination reconstructed images,an image reconstruction system based on the Internet of Things technology for wavelet-domain block compressed sensing algorithm is proposed.Establish a low-illumination image sampling model,use the image's depth-of-field adaptive adjustment method to perform wavelet-domain block compressed sensing and information fusion processing,and use a multi-scale Retinex algorithm to perform wavelet-domain block compressed sensing and information extraction to extract the information entropy of the image Feature quantity,low-illumination image enhancement processing using image adaptive enhancement method,3D information reconstruction of low-illumination image using Internet of Things technology,low-illumination image enhancement processing combined with detail enhancement method,complete design of stink dog system,and transmission Contour detection and feature reconstruction of rate maps.The simulation results show that the low-illumination image reconstruction with this method has higher resolution,better edge sensing ability,shorter reconstruction time,and higher practical application efficiency.