Defocus deblurring is a challenging task due to the spatially varying nature
of defocus blur. While deep learning approach shows great promise in solving
image restoration problems, defocus deblurring demands accurate training data
that consists of all-in-focus and defocus image pairs, which is difficult to
collect. Naive two-shot capturing cannot achieve pixel-wise correspondence
between the defocused and all-in-focus image pairs. Synthetic aperture of light
fields is suggested to be a more reliable way to generate accurate image pairs.
However, the defocus blur generated from light field data is different from
that of the images captured with a traditional digital camera. In this paper,
we propose a novel deep defocus deblurring network that leverages the strength
and overcomes the shortcoming of light fields. We first train the network on a
light field-generated dataset for its highly accurate image correspondence.
Then, we fine-tune the network using feature loss on another dataset collected
by the two-shot method to alleviate the differences between the defocus blur
exists in the two domains. This strategy is proved to be highly effective and
able to achieve the state-of-the-art performance both quantitatively and
qualitatively on multiple test sets. Extensive ablation studies have been
conducted to analyze the effect of each network module to the final
performance.

本文提出了一种新型深层次解除虚焦模糊网络，它通过使用光场技术生成高度准确的图像配对，并使用 Feat?ure loss 技术改善二次拍摄方法捕捉到的图像失焦，取得了在多个测试集上的良好表现。

使用光场生成和实际虚焦图像进行去模糊学习

Learning to Deblur using Light Field Generated and Real Defocus Images

We present an end-to-end deep learning approach to denoising speech signals
by processing the raw waveform directly. Given input audio containing speech
corrupted by an additive background signal, the system aims to produce a
processed signal that contains only the speech content. Recent approaches have
shown promising results using various deep network architectures. In this
paper, we propose to train a fully-convolutional context aggregation network
using a deep feature loss. That loss is based on comparing the internal feature
activations in a different network, trained for acoustic environment detection
and domestic audio tagging. Our approach outperforms the state-of-the-art in
objective speech quality metrics and in large-scale perceptual experiments with
human listeners. It also outperforms an identical network trained using
traditional regression losses. The advantage of the new approach is
particularly pronounced for the hardest data with the most intrusive background
noise, for which denoising is most needed and most challenging.

本研究提出了一种针对语音信号去噪的端到端深度学习方法，采用完全卷积网络训练上下文聚合并使用深度特征损失函数。通过测试表明，相较于传统回归损失函数，该方法在去除最具挑战性噪音方面效果更加显著，具有较高的客观语音质量指标和人类感知实验结果。