72 research outputs found
Burst Denoising with Kernel Prediction Networks
We present a technique for jointly denoising bursts of images taken from a
handheld camera. In particular, we propose a convolutional neural network
architecture for predicting spatially varying kernels that can both align and
denoise frames, a synthetic data generation approach based on a realistic noise
formation model, and an optimization guided by an annealed loss function to
avoid undesirable local minima. Our model matches or outperforms the
state-of-the-art across a wide range of noise levels on both real and synthetic
data.Comment: To appear in CVPR 2018 (spotlight). Project page:
http://people.eecs.berkeley.edu/~bmild/kpn
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