The goal of this paper is guided image filtering, which emphasizes the importance of structure transfer during filtering by means of an additional guidance image. Where classical guided filters transfer structures using hand-designed functions, recent guided filters have been considerably advanced through parametric learning of deep networks. The state-of-the-art leverages deep networks to estimate the two core coefficients of the guided filter. In this work, we posit that simultaneously estimating both coefficients is suboptimal, resulting in halo artifacts and structure inconsistencies. Inspired by unsharp masking, a classical technique for edge enhancement that requires only a single coefficient, we propose a new and simplified formulation of the guided filter. Our formulation enjoys a filtering prior from a low-pass filter and enables explicit structure transfer by estimating a single coefficient. Based on our proposed formulation, we introduce a successive guided filtering network, which provides multiple filtering results from a single network, allowing for a trade-off between accuracy and efficiency. Extensive ablations, comparisons and analysis show the effectiveness and efficiency of our formulation and network, resulting in state-of-the-art results across filtering tasks like upsampling, denoising, and cross-modality filtering. Code is available at https://github.com/shizenglin/Unsharp-Mask-Guided-Filtering .

中文翻译：

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非锐化蒙版引导过滤

本文的目标是引导图像滤波，它通过附加引导图像强调了滤波过程中结构转移的重要性。经典的引导滤波器使用手工设计的函数传输结构，而最近的引导滤波器通过深度网络的参数学习得到了相当大的进步。最先进的技术利用深度网络来估计引导滤波器的两个核心系数。在这项工作中，我们假设同时估计两个系数是次优的，导致光晕伪影和结构不一致。受非锐化掩蔽（一种仅需要单个系数的经典边缘增强技术）的启发，我们提出了一种新的简化的引导滤波器公式。我们的公式享有低通滤波器的先验滤波，并通过估计单个系数实现显式结构转移。基于我们提出的公式，我们引入了一个连续引导过滤网络，它从单个网络提供多个过滤结果，允许在准确性和效率之间进行权衡。广泛的消融、比较和分析显示了我们的公式和网络的有效性和效率，从而在上采样、去噪和跨模态过滤等过滤任务中产生了最先进的结果。代码可在 允许在准确性和效率之间进行权衡。广泛的消融、比较和分析显示了我们的公式和网络的有效性和效率，从而在上采样、去噪和跨模态过滤等过滤任务中产生了最先进的结果。代码可在 允许在准确性和效率之间进行权衡。广泛的消融、比较和分析显示了我们的公式和网络的有效性和效率，从而在上采样、去噪和跨模态过滤等过滤任务中产生了最先进的结果。代码可在https://github.com/shizenglin/Unsharp-Mask-Guided-Filtering .