Hyperspectral and Multispectral Image Fusion via Superpixel-Based Weighted Nuclear Norm Minimization

Integrating a low-resolution hyperspectral image and a high-resolution multispectral image is widely acknowledged as an effective approach for generating a high-resolution hyperspectral image. Recent studies have highlighted the nuclear norm as an efficient method for this problem through the utilization of low-rankness. However, the standard nuclear norm has a limitation due to treating singular values equally. To address this issue, we have incorporated the concept of the weighted nuclear norm from the image denoising problem into hyperspectral image fusion, ensuring the retention of crucial data components. Furthermore, we propose a unified framework which integrates the weighted nuclear norm, a sparse prior, and total variation regularization. This framework utilizes the ℓ 1 norm of coefficients to promote spatial-spectral sparsity in the fused images, while total variation is employed to preserve the spatial piecewise smooth structure. To efficiently solve the proposed model, we have designed an alternating direction method of multipliers. The experimental results show that our proposed approach surpasses the state-of-the-art methods.