Part of important structural edges in the image is smoothed due to the small gradients, while the others are preserved with greater gradients. Therefore, the authors propose a two-stage image smoothing method based on edge-patch histogram equalisation and patch decomposition. The authors' purpose is to increase the gradient of important structural edges while reducing the gradient of the texture region. Therefore, they divide the image into edge-patches where the structural edges are concentrated or non-edge-patches where the texture details are concentrated by image segmentation. The edge-patch needs to be equalised by the histograms for increasing the gradient of the edge pixels. All patches are decomposed to extract the smooth component for reducing the gradient of pixels. The smooth component of each patch is smoothed via $L_0$L0 gradient minimisation. In order to ensure the continuity of the patch boundaries, the edge-patch is inversely equalised. Finally, the whole image is smoothed via $L_0$L0 gradient minimisation for removing residual textures and seams. Experimental results demonstrate that the proposed method is more competitive in maintaining important structural edges and removing texture details than the state-of-the-art approaches. The proposed method can be applied to many areas of image processing.

中文翻译：

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基于边缘补丁直方图均衡和补丁分解的两阶段图像平滑

图像中的重要结构边缘的一部分由于较小的渐变而变得平滑，而其余部分则以较大的渐变保留。因此，作者提出了一种基于边缘补丁直方图均衡和补丁分解的两阶段图像平滑方法。作者的目的是增加重要结构边缘的梯度，同时减小纹理区域的梯度。因此，它们将图像分为边缘集中的边缘斑块（结构边缘集中）或非边缘边缘的纹理细节通过图像分割集中。边缘斑块需要通过直方图进行均衡，以增加边缘像素的梯度。分解所有面片以提取平滑分量，以减小像素的梯度。每个贴片的平滑分量通过$ L_0 $大号0梯度最小化。为了确保斑块边界的连续性，边缘斑块被反均衡。最后，通过$ L_0 $大号0梯度最小化以去除残留的纹理和接缝。实验结果表明，与最新技术相比，该方法在保持重要的结构边缘和去除纹理细节方面更具竞争力。所提出的方法可以应用于图像处理的许多领域。