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State Denoised Recurrent Neural Networks
We investigate the use of attractor neural networks for denoising the internal states of another neural network, thereby boosting its generalization performance. Denoising is most promising for recurrent sequence-processing networks (i.e. recurrent neural networks), in which noise can accumulate in the hidden states over the elements of a sequence. We call our architecture state- denoised recurrent neural network (SD-RNN). We conduct a series of experiments to demonstrate the benefit of internal denoising, from small experiments like detecting parity of a binary sequence to larger natural language processing data sets. We characterize the behavior of the network using an information theoretic analysis, and we show that internal denoising causes the network to learn better on less data
Deep Burst Denoising
Noise is an inherent issue of low-light image capture, one which is
exacerbated on mobile devices due to their narrow apertures and small sensors.
One strategy for mitigating noise in a low-light situation is to increase the
shutter time of the camera, thus allowing each photosite to integrate more
light and decrease noise variance. However, there are two downsides of long
exposures: (a) bright regions can exceed the sensor range, and (b) camera and
scene motion will result in blurred images. Another way of gathering more light
is to capture multiple short (thus noisy) frames in a "burst" and intelligently
integrate the content, thus avoiding the above downsides. In this paper, we use
the burst-capture strategy and implement the intelligent integration via a
recurrent fully convolutional deep neural net (CNN). We build our novel,
multiframe architecture to be a simple addition to any single frame denoising
model, and design to handle an arbitrary number of noisy input frames. We show
that it achieves state of the art denoising results on our burst dataset,
improving on the best published multi-frame techniques, such as VBM4D and
FlexISP. Finally, we explore other applications of image enhancement by
integrating content from multiple frames and demonstrate that our DNN
architecture generalizes well to image super-resolution
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