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Panchromatic Side Sparsity Model Based Deep Unfolding Network for Pansharpening

Deep learning recently receives state of the art results in pansharpening. However, most of existing pansharpening neural networks are purely data-driven, without taking account of the characteristics of pansharpening task. To address this issue, we propose a novel deep unfolding network for pansharpening that combines the insight of variational optimization model and the capability of deep neural network. We firstly develop a Panchromatic Side Sparsity (PASS) prior based variational optimization model for pansharpend image reconstruction, which is formulated as the ℓ1-ℓ1 minimization. In particular, the PASS prior is defined using the transform sparsity, which can alleviate the influences of the irregular outliers between the multispectral (MS) and panchromatic (PAN) images. The iterations of half-quadratic splitting algorithm for solving the ℓ1-ℓ1 minimization are then deeply unfolded into a deep neural network, referred as PASS-Net. To capture the nonlinear relationship between MS and PAN images, the linear transforms used in PASS prior are extended into the subnetworks in PASS-Net. Moreover, a pair of learnable downsampling and upsampling modules are designed to realize the downsampling and upsampling operations, which can improve the flexibility. The experimental results on different satellites datasets confirm that PASS-Net is superior to some representational traditional methods and state-of-the-art deep learning based methods.