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Real-World Image Denoising with Deep Boosting

机译：真实世界的图像去噪增压

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摘要

We propose a Deep Boosting Framework (DBF) for real-world image denoising by integrating the deep learning technique into the boosting algorithm. The DBF replaces conventional handcrafted boosting units by elaborate convolutional neural networks, which brings notable advantages in terms of both performance and speed. We design a lightweight Dense Dilated Fusion Network (DDFN) as an embodiment of the boosting unit, which addresses the vanishing of gradients during training due to the cascading of networks while promoting the efficiency of limited parameters. The capabilities of the proposed method are first validated on several representative simulation tasks including non-blind and blind Gaussian denoising and JPEG image deblocking. We then focus on a practical scenario to tackle with the complex and challenging real-world noise. To facilitate leaning-based methods including ours, we build a new Real-world Image Denoising (RID) dataset, which contains 200 pairs of high-resolution images with diverse scene content under various shooting conditions. Moreover, we conduct comprehensive analysis on the domain shift issue for real-world denoising and propose an effective one-shot domain transfer scheme to address this issue. Comprehensive experiments on widely used benchmarks demonstrate that the proposed method significantly surpasses existing methods on the task of real-world image denoising. Code and dataset are available at https://github.comgchc/deepBoosting.
机译：通过将深度学习技术集成到升压算法中，我们提出了一个深层升压框架（DBF），以实现真实的图像去噪。 DBF通过详细说明的卷积神经网络取代了传统的手工升压单元，这在性能和速度方面带来了显着的优势。我们设计了一种轻量密度扩张的融合网络（DDFN）作为升压单元的实施例，其在训练期间由于网络的级联而在促进有限参数的效率的同时解决训练期间的梯度消失。在包括非盲目和盲的高斯去噪和JPEG图像去块的几个代表性仿真任务中首次验证了所提出的方法的能力。然后，我们专注于实际的场景，以解决复杂和挑战的真实噪音。为了促进基于倾斜的方法，我们建立了一个新的真实世界图像去噪（RID）数据集，其中包含200对具有不同场景内容的高分辨率图像，在各种拍摄条件下。此外，我们对现实世界去噪提供综合分析，并提出了一个有效的一次性域转移计划来解决这个问题。广泛使用的基准的综合实验表明，该方法的方法显着超越了现有的现实世界图像去噪的方法。代码和数据集可在https://github.comgchc/deepboosting中获得。

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