Due to the powerful edge-preserving ability and low computational complexity, Guided image filter (GIF) and its improved versions has been widely applied in computer vision and image processing. However, all of them are suffered halo artifacts to some degree, as the regularization parameter increase. In the case of inconsistent structure of guidance image and input image, edge-preserving ability degradation will also happen. In this paper, a novel guided image filter is proposed by integrating an explicit first-order edge-protect constraint and an explicit residual constraint which will improve the edge-preserving ability in both cases. To illustrate the efficiency of the proposed filter, the performances are shown in some typical applications, which are single image detail enhancement, multi-scale exposure fusion, hyper spectral images classification. Both theoretical analysis and experimental results prove that the powerful edge-preserving ability of the proposed filter.

翻译：由于引导图像滤波（GIF）及其改进版本具有强大的边缘保持能力和较低的计算复杂度，已在计算机视觉和图像处理领域得到广泛应用。然而，随着正则化参数的增加，这些方法均会不同程度地出现光晕伪影。当引导图像与输入图像结构不一致时，边缘保持能力也会下降。本文提出一种新型引导图像滤波方法，通过整合显式一阶边缘保护约束和显式残差约束，在两种情况下均能提升边缘保持能力。为验证所提滤波器的有效性，在单图像细节增强、多尺度曝光融合、高光谱图像分类等典型应用中展示了其性能。理论分析与实验结果均证明了该滤波器强大的边缘保持能力。