发帖
雷达卡
摘要翻译：
边缘直方图规范的目标是找到一个边缘图像的直方图与给定的边缘直方图尽可能匹配的图像。Mignotte提出了一个非凸模型[M.Mignotte.图像边缘直方图规范问题的基于能量的模型。IEEE图像处理学报，21(1):379--386,2012]。在他的工作中，输入图像的边缘大小首先通过直方图规范修改，以匹配给定的边缘直方图。然后，对非凸模型进行最小化，找到边缘直方图与修改后的边缘直方图匹配的输出图像。模型的非凸性阻碍了计算和包含有用的约束，如动态范围约束。在本文中，我们不考虑边缘大小，而是直接考虑图像梯度，并提出了一种基于梯度的凸模型。此外，我们基于不同的应用在我们的模型中包含了额外的约束。该模型的凸性使得我们可以使用乘法器交替方向法或快速迭代收缩阈值算法来高效地计算输出图像。我们考虑了在边缘保持平滑中的几个应用，包括图像提取、边缘提取、细节夸张和文档扫描移除。数值结果表明，我们的方法成功地得到了令人满意的结果。
---
英文标题：
《A Convex Model for Edge-Histogram Specification with Applications to
Edge-preserving Smoothing》
---
作者：
Kelvin C.K. Chan, Raymond H. Chan, Mila Nikolova
---
最新提交年份：
2018
---
分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Image and Video Processing 图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
--
---
英文摘要：
The goal of edge-histogram specification is to find an image whose edge image has a histogram that matches a given edge-histogram as much as possible. Mignotte has proposed a non-convex model for the problem [M. Mignotte. An energy-based model for the image edge-histogram specification problem. IEEE Transactions on Image Processing, 21(1):379--386, 2012]. In his work, edge magnitudes of an input image are first modified by histogram specification to match the given edge-histogram. Then, a non-convex model is minimized to find an output image whose edge-histogram matches the modified edge-histogram. The non-convexity of the model hinders the computations and the inclusion of useful constraints such as the dynamic range constraint. In this paper, instead of considering edge magnitudes, we directly consider the image gradients and propose a convex model based on them. Furthermore, we include additional constraints in our model based on different applications. The convexity of our model allows us to compute the output image efficiently using either Alternating Direction Method of Multipliers or Fast Iterative Shrinkage-Thresholding Algorithm. We consider several applications in edge-preserving smoothing including image abstraction, edge extraction, details exaggeration, and documents scan-through removal. Numerical results are given to illustrate that our method successfully produces decent results efficiently.
---
PDF链接：
https://arxiv.org/pdf/1806.08101
京公网安备 11010802022788号
