论文标题
高效$ \ ell^0 $基于梯度的超级分辨率，用于简化图像分割
Efficient $\ell^0$ gradient-based Super Resolution for simplified image segmentation
论文作者
论文摘要
我们考虑了基于目标图像梯度稀疏的假设，我们考虑了单形图像超分辨率的变异模型。我们通过考虑各向同性和各向异性$ \ ell^0 $正则化来实施这一假设，与[1]中的一般信号恢复问题相似。为了实现模型的数值实现，我们提出了一种新型有效的ADMM拆分算法，其取代溶液是通过硬质量和标准的共轭分量求解器有效地计算出来的。我们测试了高度衰减的合成和现实世界数据的模型，并通过几种变异方法以及最新的深度学习技术进行定量比较我们的结果。我们的实验表明，与其他方法相比，当应用于QR和细胞检测中时，可以有效地使用$ \ ell^0 $梯度注册的超级分辨图像，以提高标准分割算法的准确性，以及与其他方法获得的结果相比。
We consider a variational model for single-image super-resolution based on the assumption that the gradient of the target image is sparse. We enforce this assumption by considering both an isotropic and an anisotropic $\ell^0$ regularisation on the image gradient combined with a quadratic data fidelity, similarly as studied in [1] for general signal recovery problems. For the numerical realisation of the model, we propose a novel efficient ADMM splitting algorithm whose substeps solutions are computed efficiently by means of hard-thresholding and standard conjugate-gradient solvers. We test our model on highly-degraded synthetic and real-world data and quantitatively compare our results with several variational approaches as well as with state-of-the-art deep-learning techniques. Our experiments show that $\ell^0$ gradient-regularised super-resolved images can be effectively used to improve the accuracy of standard segmentation algorithms when applied to QR and cell detection, and landcover classification problems, in comparison to the results achieved by other approaches.
