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Although high-resolution hyperspectral images are of great significance in various applications, the manner in which to acquire such images remains a fundamental problem due to sensors’ physical limitations. Fusing high-spatial resolution multispectral images (HR-MSIs) with low-spatial resolution hyperspectral images (LR-HSIs) has become a common approach to increase the spatial resolution of hyperspectral images (HSIs). Various fusion methods have been developed to obtain images with high resolution in both the spatial and spectral domains; the main drawbacks of these methods are time consumption and spectral distortion. With the intention of developing a fast yet accurate image fusion algorithm, this paper is attempted to incorporate the benefits of deep learning and convolutional sparse coding to create a new fusion method. First, the inputs are initialized by solving the Sylvester equation from an up-sampled LR-HSI and an HR-MSI. Second, the learned iterative shrinkage thresholding algorithm is extended to its convolutional version as the central part of the model and recursively learn the sparse priors. Finally, a fused image is achieved by adding the residual to the initialized image. The proposed method is conducted on the widely used datasets (Harvard and iCVL). The experimental results are demonstrated that the proposed approach outperforms several state-of-the-art approaches in terms of quantitative and qualitative perspectives.

A model-guided deep convolutional sparse coding network for hyperspectral and multispectral image fusion