{'fraction_non_alphanumeric': 0.02328159645232816, 'fraction_numerical': 0.0, 'mean_word_length': 5.496402877697841, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Pan-Sharpening with Hessian Nuclear Norm Induced Spatial Consistency

In this paper, we propose a new variational pan-sharpening method with Hessian nuclear norm induced spatial consistency, which aims to fuse a low resolution (LR) multispectral (MS) image and a high resolution (HR) panchromatic (Pan) image into an HR MS image. In addition to using the fidelity based local spectral consistency term for preserving the spectral information, we particularly exploit the Hessian feature consistence between the HR MS image and Pan image, and propose a new Hessian nuclear norm induced spatial consistency term for preserving the spatial information. Then, the proposed model is solved by an efficient algorithm under the FISTA framework. Finally, the experimental results demonstrate that the proposed method outperforms various pan-sharpening methods in terms of higher spectral and spatial qualities.