论文标题
商空间中的歧管建模：学习图像补丁的可分解性不变映射
Manifold Modeling in Quotient Space: Learning An Invariant Mapping with Decodability of Image Patches
论文作者
论文摘要
这项研究提出了一个使用等价类别的概念来对图像贴片进行流形学习的框架：商空间中的歧管建模（MMQ）。在MMQ中，我们不考虑图像的一组本地贴片，而是通过引入等价类的概念并在其规范贴片上执行多种多样的学习来获得的典型补丁集。规范补丁代表等效类，其自动编码器在商空间中构造了一个歧管。基于此框架，我们通过引入旋转 - 绑带等效关系来生成一种新型的基于多种歧管的图像模型。此外，我们通过将提出的图像模型拟合到损坏的观察到的图像并得出算法来求解它来提出图像重建问题。我们的实验表明，提出的图像模型对于各种自我监督的图像重建任务有效，例如图像插入，deblurring，deblurring，super-solution and deoinging。
This study proposes a framework for manifold learning of image patches using the concept of equivalence classes: manifold modeling in quotient space (MMQS). In MMQS, we do not consider a set of local patches of the image as it is, but rather the set of their canonical patches obtained by introducing the concept of equivalence classes and performing manifold learning on their canonical patches. Canonical patches represent equivalence classes, and their auto-encoder constructs a manifold in the quotient space. Based on this framework, we produce a novel manifold-based image model by introducing rotation-flip-equivalence relations. In addition, we formulate an image reconstruction problem by fitting the proposed image model to a corrupted observed image and derive an algorithm to solve it. Our experiments show that the proposed image model is effective for various self-supervised image reconstruction tasks, such as image inpainting, deblurring, super-resolution, and denoising.
