Better Image Filter for Pansharpening.

Abstract

The modulation transfer function tailored image filter (MTF-TIF) has long been regarded as the optimal filter for multispectral image pansharpening. It excels at simulating the camera's frequency response, thereby capturing finer image details and significantly improving pansharpening performance. However, we are skeptical about whether the pre-measured MTF is sufficient to describe the characteristics of actually acquired panchromatic image (PAN) and multispectral image (MSI). For example, any image resampling operations in geometric correction or image registration inevitably change the sharpness of acquired PAN and MSI, and the processed images no longer conform to the camera's MTF. Further, following the Wald protocol, in deep learning (DL) methods using MTF-TIF for downsampling images to construct training data does not satisfy the generalization consistency of training and testing. To prove our point, we propose a pair of symmetric frameworks based on DL in this paper, to find better image filters suitable for both traditional and DL pansharpening methods. We embed two learnable filters into the frameworks to simulate the optimal image filter, namely anisotropic Gaussian image filter and arbitrary image filter. Further, the proposed frameworks can capture subtle offsets between images and maintain the smoothness of the global deformation field. Extensive experiments on various satellite datasets demonstrate that the proposed frameworks can find better image filters than MTF-TIFs, which can achieve better pansharpening performance with stronger generalization ability.