论文标题
自适应加权引导图像过滤，以增强形状 - 焦虑的深度
Adaptive Weighted Guided Image Filtering for Depth Enhancement in Shape-From-Focus
论文作者
论文摘要
现有的焦点（SFF）技术无法从一系列多聚焦图像中保留深度边缘和精细的结构细节。此外，多聚焦图像序列中的噪声会影响深度图的准确性。在本文中，提出了一种基于自适应加权引导图像过滤（AWGIF）的新型SFF的深度增强算法，以解决上述问题。 AWGIF用于分解一个初始深度图，该深度图由传统的SFF估计为基础层和细节层。为了准确地保留在精制的深度图中的边缘，指导图像是根据多聚焦图像序列构造的，并且AWGIF的系数被用来抑制噪声，同时增强了精细的深度细节。对真实和合成对象的实验证明了拟议算法在反噪声方面的优越性，并且与现有方法相比，保持深度边缘和精细的结构细节的能力。
Existing shape from focus (SFF) techniques cannot preserve depth edges and fine structural details from a sequence of multi-focus images. Moreover, noise in the sequence of multi-focus images affects the accuracy of the depth map. In this paper, a novel depth enhancement algorithm for the SFF based on an adaptive weighted guided image filtering (AWGIF) is proposed to address the above issues. The AWGIF is applied to decompose an initial depth map which is estimated by the traditional SFF into a base layer and a detail layer. In order to preserve the edges accurately in the refined depth map, the guidance image is constructed from the multi-focus image sequence, and the coefficient of the AWGIF is utilized to suppress the noise while enhancing the fine depth details. Experiments on real and synthetic objects demonstrate the superiority of the proposed algorithm in terms of anti-noise, and the ability to preserve depth edges and fine structural details compared to existing methods.
