Self-adaptive sampling rate assignment and image reconstruction via combination of structured sparsity and non-local total variation priors |
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| Affiliation: | 1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi''an 710071, China;2. Department of Mathematics, Xidian University, Xi''an 710071, China;1. School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, China;2. School of Petroleum Engineering, Changzhou University, Changzhou 213164, China;1. Instituto Nacional de Astrofisica, Optica y Electronica, Luis Enrique Erro 1, Santa Maria Tonantzintla, Puebla, Mexico;1. School of Electronic Information and Electric Engineering, Shangluo University, Shangluo 726000, China;2. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, International Collaboration Joint Lab in Intelligent Perception and Computation, Xidian University, Xi’an 710071, China;3. School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China;4. School of Information Science and Technology, Northwest University, Xi’an 710127, China;1. College of Intelligence and Computing, Tianjin University, Tianjin, China;2. Tianjin Key Laboratory of Advanced Network Technology and Application, Tianjin, China;3. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;4. Key Laboratory of Grain Information Processing and Control, Henan University of Technology, Ministry of Education, Henan, China;5. School of Computer Science and Technology, University of Science and Technology of China, Hefei, China;6. School of Mathematics, Tianjin University, Tianjin, China;7. School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China |
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| Abstract: | Compressive sensing (CS) is an emerging approach for acquisition of sparse or compressible signals. For natural images, block compressive sensing (BCS) has been designed to reduce the size of sensing matrix and the complexity of sampling and reconstruction. On the other hand, image blocks with varying structures are too different to share the same sampling rate and sensing matrix. Motivated by this, a novel framework of adaptive acquisition and reconstruction is proposed to assign sampling rate adaptively. The framework contains three aspects. First, a small part of sampling rate is employed to pre-sense each block and a novel approach is proposed to estimate its compressibility only from pre-sensed measurements. Next, two assignment schemes are proposed to assign the other part of the sampling rate adaptively to each block based on its estimated compressibility. A higher sampling rate is assigned to incompressible blocks but a lower one to compressible ones. The sensing matrix is constructed based on the assigned sampling rates. The pre-sensed measurements and the adaptive ones are concatenated to form the final measurements. Finally, it is proposed that the reconstruction is modeled as a multi-objects optimization problem which involves the structured sparsity and the non-local total variation prior together. It is simplified into a 3-stage alternating optimization problem and is solved by an augmented Lagrangian method. Experiments on four categories of real natural images and medicine images demonstrate that the proposed framework captures local and nonlocal structures and outperforms the state-of-the-art methods. |
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| Keywords: | Compressive sampling Assignment of sampling rate Compressibility Structured sparsity Non-local total variation |
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