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Burst Denoising with Kernel Prediction Networks

Ben Mildenhall
Jiawen Chen
Dillon Sharlet
Ren Ng
Rob Carroll
CVPR (2018) (to appear)


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.