计算机科学
迭代重建
人工智能
卷积神经网络
计算机视觉
块(置换群论)
像素
噪音(视频)
重建算法
迭代法
算法
图像(数学)
数学
几何学
作者
Kuldeep Kulkarni,Suhas Lohit,Pavan Turaga,Ronan Kerviche,Amit Ashok
出处
期刊:Computer Vision and Pattern Recognition
日期:2016-06-01
被引量:590
摘要
The goal of this paper is to present a non-iterative and more importantly an extremely fast algorithm to reconstruct images from compressively sensed (CS) random measurements. To this end, we propose a novel convolutional neural network (CNN) architecture which takes in CS measurements of an image as input and outputs an intermediate reconstruction. We call this network, ReconNet. The intermediate reconstruction is fed into an off-the-shelf denoiser to obtain the final reconstructed image. On a standard dataset of images we show significant improvements in reconstruction results (both in terms of PSNR and time complexity) over state-of-the-art iterative CS reconstruction algorithms at various measurement rates. Further, through qualitative experiments on real data collected using our block single pixel camera (SPC), we show that our network is highly robust to sensor noise and can recover visually better quality images than competitive algorithms at extremely low sensing rates of 0.1 and 0.04. To demonstrate that our algorithm can recover semantically informative images even at a low measurement rate of 0.01, we present a very robust proof of concept real-time visual tracking application.
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