论文标题
美元
$\ell_1$DecNet+: A new architecture framework by $\ell_1$ decomposition and iteration unfolding for sparse feature segmentation
论文作者
论文摘要
$ \ ell_1 $基于稀疏正则化在压缩感测和图像处理中起着核心作用。在本文中，我们提出了$ \ ell_1 $ decnet，这是一个源自差异分解模型的展开网络，该网络包含了$ \ ell_1 $相关的稀疏正则化，并通过尺度交替的乘数方法（ADMM）解决。 $ \ ell_1 $ decnet有效地将输入图像分解为稀疏功能和学习的密集功能，从而有助于随后的稀疏功能相关的操作。基于此，我们开发了$ \ ell_1 $ decnet+，这是一个可学习的体系结构框架，由我们的$ \ ell_1 $ decnet和一个分段模块组成，该模块可以通过提取的稀疏功能而不是原始图像进行操作。该体系结构很好地结合了数学建模和数据驱动方法的好处。据我们所知，这是第一项将数学图像先验纳入分割网络结构中的特征提取中的研究。此外，我们的$ \ ell_1 $ decnet+框架可以轻松扩展到3D情况。我们评估了$ \ ell_1 $ decnet+对两个通常遇到的稀疏分段任务的有效性：医疗图像处理中的视网膜船分段和工业异常鉴定中的路面裂纹检测。不同数据集上的实验结果表明，我们的$ \ ell_1 $ decnet+架构具有各种轻量级分割模块可以分别比其扩大版本的相等或更好的性能。这在资源有限的设备上具有特别实际的优势。
$\ell_1$ based sparse regularization plays a central role in compressive sensing and image processing. In this paper, we propose $\ell_1$DecNet, as an unfolded network derived from a variational decomposition model incorporating $\ell_1$ related sparse regularization and solved by scaled alternating direction method of multipliers (ADMM). $\ell_1$DecNet effectively decomposes an input image into a sparse feature and a learned dense feature, and thus helps the subsequent sparse feature related operations. Based on this, we develop $\ell_1$DecNet+, a learnable architecture framework consisting of our $\ell_1$DecNet and a segmentation module which operates over extracted sparse features instead of original images. This architecture combines well the benefits of mathematical modeling and data-driven approaches. To our best knowledge, this is the first study to incorporate mathematical image prior into feature extraction in segmentation network structures. Moreover, our $\ell_1$DecNet+ framework can be easily extended to 3D case. We evaluate the effectiveness of $\ell_1$DecNet+ on two commonly encountered sparse segmentation tasks: retinal vessel segmentation in medical image processing and pavement crack detection in industrial abnormality identification. Experimental results on different datasets demonstrate that, our $\ell_1$DecNet+ architecture with various lightweight segmentation modules can achieve equal or better performance than their enlarged versions respectively. This leads to especially practical advantages on resource-limited devices.
