混合稀疏先验算法在图像复原中的应用 学习满足稀疏编码

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摘要翻译：
现有的图像复原方法可以分为基于模型的方法和基于学习的方法。前者以稀疏编码技术为代表，致力于利用关于未知高分辨率图像的内在先验知识；而后者--由最近开发的深度学习技术推广--从一些训练数据集中利用外部图像先验。探索他们的中间立场，追求一种能够在两个世界中达到最佳的混合形象是很自然的。本文提出了一种实现这一目标的系统方法，称为结构化分析稀疏编码(SASC)。具体地说，通过深度卷积神经网络从外部训练数据中学习结构化稀疏先验（与以前基于学习的方法类似）；同时，从输入观测图像内部估计另一个结构化稀疏先验（类似于以前的基于模型的方法）。然后将两个结构化的稀疏先验组合起来，产生一个混合先验，将来自两个领域的知识结合在一起。为了管理计算复杂度，我们开发了一个新的框架，通过深度卷积神经网络实现混合结构稀疏编码过程。实验结果表明，本文提出的混合图像复原方法与现有的图像复原方法相比具有相当的性能，而且往往优于现有的图像复原方法。
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英文标题：
《Learning Hybrid Sparsity Prior for Image Restoration: Where Deep
  Learning Meets Sparse Coding》
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作者：
Fangfang Wu, Weisheng Dong, Guangming Shi and Xin Li
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最新提交年份：
2018
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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。
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一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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英文摘要：
  State-of-the-art approaches toward image restoration can be classified into model-based and learning-based. The former - best represented by sparse coding techniques - strive to exploit intrinsic prior knowledge about the unknown high-resolution images; while the latter - popularized by recently developed deep learning techniques - leverage external image prior from some training dataset. It is natural to explore their middle ground and pursue a hybrid image prior capable of achieving the best in both worlds. In this paper, we propose a systematic approach of achieving this goal called Structured Analysis Sparse Coding (SASC). Specifically, a structured sparse prior is learned from extrinsic training data via a deep convolutional neural network (in a similar way to previous learning-based approaches); meantime another structured sparse prior is internally estimated from the input observation image (similar to previous model-based approaches). Two structured sparse priors will then be combined to produce a hybrid prior incorporating the knowledge from both domains. To manage the computational complexity, we have developed a novel framework of implementing hybrid structured sparse coding processes by deep convolutional neural networks. Experimental results show that the proposed hybrid image restoration method performs comparably with and often better than the current state-of-the-art techniques.
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PDF链接：
https://arxiv.org/pdf/1807.0692

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关键词：Applications Experimental Presentation Mathematical Architecture 数据 based 图像 学习 基于