Ah ! Another instance of the Great Convergence. This time it is by using the measurement step in compressive sensing as the first layer of a neural network. Intsead of a traditional iterative solver for the reconstruction or the simple one-through least squares step of ELMs, the authors go for a deep architecture much like recently.
Why is this study a big deal ? For the longest time, people in Deep Learning have been using convolutional steps in order to remove the symmetry associated with translation (the Fourier transform allows the comparison of objects translated from one another easily). In this paper, the authors show that, you can break that symmetry early on the very first layers (here it is layer zero) and still do well. This is a big deal in my view.
Why is this study a big deal ? For the longest time, people in Deep Learning have been using convolutional steps in order to remove the symmetry associated with translation (the Fourier transform allows the comparison of objects translated from one another easily). In this paper, the authors show that, you can break that symmetry early on the very first layers (here it is layer zero) and still do well. This is a big deal in my view.
Woohoo !
ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Random Measurements by Kuldeep Kulkarni, Suhas Lohit, Pavan Turaga, Ronan Kerviche, Amit Ashok
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.h/t Thomas
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