Joint Convolutional Analysis and Synthesis Sparse Representation for Single Image Layer Separation

Shuhang Gu, Deyu Meng, W. Zuo, Lei Zhang
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引用次数: 167

Abstract

Analysis sparse representation (ASR) and synthesis sparse representation (SSR) are two representative approaches for sparsity-based image modeling. An image is described mainly by the non-zero coefficients in SSR, while is mainly characterized by the indices of zeros in ASR. To exploit the complementary representation mechanisms of ASR and SSR, we integrate the two models and propose a joint convolutional analysis and synthesis (JCAS) sparse representation model. The convolutional implementation is adopted to more effectively exploit the image global information. In JCAS, a single image is decomposed into two layers, one is approximated by ASR to represent image large-scale structures, and the other by SSR to represent image fine-scale textures. The synthesis dictionary is adaptively learned in JCAS to describe the texture patterns for different single image layer separation tasks. We evaluate the proposed JCAS model on a variety of applications, including rain streak removal, high dynamic range image tone mapping, etc. The results show that our JCAS method outperforms state-of-the-arts in these applications in terms of both quantitative measure and visual perception quality.
单幅图像层分离的联合卷积分析与合成稀疏表示
分析稀疏表示(ASR)和综合稀疏表示(SSR)是基于稀疏性的图像建模的两种代表性方法。在SSR中,图像主要由非零系数来描述,而在ASR中,图像主要由零指数来表征。为了利用ASR和SSR的互补表示机制,我们将这两个模型整合在一起,提出了一个联合卷积分析与合成(JCAS)稀疏表示模型。采用卷积实现,更有效地利用图像全局信息。在JCAS中,将单幅图像分解为两层,一层由ASR近似表示图像的大尺度结构,另一层由SSR近似表示图像的细尺度纹理。JCAS自适应学习合成字典来描述不同单幅图像层分离任务的纹理模式。我们对所提出的JCAS模型进行了多种应用评估,包括去除雨纹、高动态范围图像色调映射等。结果表明,我们的JCAS方法在定量测量和视觉感知质量方面都优于目前最先进的应用。
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