Journal
IEEE ACCESS
Volume 9, Issue -, Pages 158695-158709Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3130973
Keywords
Image reconstruction; Neural networks; Training; Task analysis; Deep learning; Convolutional neural networks; Licenses; Compressive sensing; convolutional neural network; deep learning; inverse problems; optimization methods
Categories
Funding
- Ministry of Science and Technology [MOST 107-2221-E-001-015-MY2, MOST 109-2221-E-001-023]
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A learning-based method termed GPX-ADMM-Net is proposed for solving image compressive sensing problems, achieving high performance and adaptivity to measurement rates and cross-task tasks, such as other image inverse problems.
The building of effective neural network architectures for solving image compressive sensing (CS) problems is a challenge. Hence, it is helpful to consult the structural insight provided by traditional optimization algorithms, the results of which are interpretable. Inspired by this concept, we propose a fast, light, easily interpreted, and relatively stable learning-based method for solving image CS problems. This method is termed the Generalized ProXimal Alternating Direction Method of Multipliers Network (GPX-ADMM-Net) and can be used to solve image CS problems that conventionally are solved using optimization algorithms with intensive computations. GPX-ADMM-Net features the following characteristics: (1) It achieves a higher or comparable performance with state-of-the-art learning-based methods for the image CS problem in terms of reconstruction quality, running speed, and storage cost. (2) Because our method strictly follows the insight of optimization algorithms, it possesses a measurement rate (MR) adaptivity able to reconstruct acceptable image qualities under different MRs with only a single set of trained parameters, which is a unique property in learning-based methods. (3) Despite its dedicated design for the image CS problem, it possesses a cross-task adaptivity extended to handle other image inverse problems (e.g., super-resolution and inpainting) that are treated as a special case of CS. Extensive experimental results validate the above claims.
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