资源分类
》 资源 》论文》Burst Denoising with Kernel Prediction Networks

Burst Denoising with Kernel Prediction Networks

2019-10-17 | |  67 |   40 |   0
Abstract 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 stateof-the-art across a wide range of noise levels on both real and synthetic data

上一篇：Boundary Flow: A Siamese Network that Predicts Boundary Motion without Training on Motion

下一篇：CartoonGAN: Generative Adversarial Networks for Photo Cartoonization

用户评价
全部评价

热门资源

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to learn...

    The move from hand-designed features to learned...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...