Pansharpening refers to the fusion of a panchromatic (PAN) and a multispectral (MS) image aimed at generating a high-quality outcome over the same area. This particular image fusion problem has been widely studied, but until recently, it has been challenging to balance the spatial and spectral fidelity in fused images. The spectral distortion is widespread in the component substitution-based approaches due to the variation in the intensity distribution of spatial components. We lightened the idea using the total variation optimization to improve upon a novel GIHS-TV framework for pansharpening. The framework drew the high spatial fidelity from the GIHS scheme and implemented it with a simpler variational expression. An improved L1-TV constraint to the new spatial–spectral information was introduced to the GIHS-TV framework, along with its fast implementation. The objective function was solved by the Iteratively Reweighted Norm (IRN) method. The experimental results on the “PAirMax” dataset clearly indicated that GIHS-TV could effectively reduce the spectral distortion in the process of component substitution. Our method has achieved excellent results in visual effects and evaluation metrics.

This paper presents an optimal design method to solve the initial optical parameters, which is used for the common-aperture design including transmission and reflection system. On the basis of the particle swarm optimization algorithm, the self-adaptive weight and multi-level screening principle are introduced, which effectively improves the local and global searching ability in the non-linear space. Utilizing the third-order aberration and achromatic theories, the optimal initial structural parameters satisfying the objective function can be quickly calculated. We propose a common-aperture architecture and an illustrative optical design to demonstrate the concept. The system has the ability of field of view segmentation and optical path segmentation, and realizes the three-dimensional compact optical system layout. Simulations of ray tracing acquisition and imaging performances demonstrate the potential of adaptive particle swarm optimization algorithms for flexibility and simplicity in common-aperture imaging design.