Abstract
We propose a convolutional sparse coding (CSC) for super resolution (CSC-SR) algorithm with a joint Bayesian learning strategy. Due to the unknown parameters in solving CSC-SR, the performance of the algorithm depends on the choice of the parameter. To this end, a coupled Beta-Bernoulli process is employed to infer appropriate filters and sparse coding maps (SCM) for both low resolution (LR) image and high resolution (HR) image. The filters and the SCMs are learned in a joint inference. The experimental results validate the advantages of the proposed approach over the previous CSC-SR and other state-of-the-art SR methods.
Highlights
Image super-resolution (SR) arms to reconstruct a high resolution (HR) image from a single or several low resolution (LR) image of the scene together [1,2,3,4,5]
The resolution limitations of low-cost imaging sensors is overcome by SR methods, and the degradation in the LR images caused by blur and the motion of camera or scene are utilized to reconstruct the HR image by SR methods
We present a convolutional sparse coding based super resolution method with a joint Bayesian learning strategy (JB-CSC)
Summary
Image super-resolution (SR) arms to reconstruct a high resolution (HR) image from a single or several low resolution (LR) image of the scene together [1,2,3,4,5]. By processing the global image rather than in each local patch, the previous CSC-SR has shown its outperformance over the sparse coding (SC) based ones. CSC-SR adopts a more adaptive decomposition-reconstruction strategy, this model still utilizes the fixed number of filters These parameters should be assigned a priori and a structure of latent space representing the input data should be applied to solve CSC problem. We learn the filters and the sparse coding maps adaptively by modeling them using a Beta-Bernoulli distribution in decomposing the LR image. Since learning processes on LR image and HR image are based on the same distribution, the filters and the sparse coding maps in each stage are inferred simultaneously under a joint inference process. The experimental results of the proposed method validate the competitive results with the state-of-the-art methods
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