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In all-day-all-weather tasks, well-aligned multi-modality images pairs can provide extensive complementary information for image-guided UAV target detection. However, multi-modality images in real scenarios are often misaligned, and images registration is extremely difficult due to spatial deformation and the difficulty narrowing cross-modality discrepancy. To better overcome the obstacle, in this paper, we construct a General Cross-Modality Registration (GCMR) Framework, which explores generation registration pattern to simplify the cross-modality image registration into a easier mono-modality image registration with an Image Cross-Modality Translation Network (ICMTN) module and a Multi-level Residual Dense Registration Network (MRDRN). Specifically, ICMTN module is used to generate a pseudo infrared image taking a visible image as input and correct the distortion of structural information during the translation of image modalities. Benefiting from the favorable geometry correct ability of the ICMTN, we further employs MRDRN module which can fully extract and exploit the mutual information of misaligned images to better registered Visible and Infrared image in a mono-modality setting. We evaluate five variants of our approach on the public Anti-UAV datasets. The extensive experimental results demonstrate that the proposed architecture achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

• Proposing a novel method benefiting from both compressive sensing and deep learning. • Introducing a discriminative dictionary learning method to better characterize images. • Generating a more accurate input interpolated low-resolution image for neural network. • Constructing a deep residual network to capture more high-frequency information. Image super-resolution successfully overcomes the limitation of infrared imaging system and satisfies the increasing demand for high-resolution infrared images. In this paper, we propose a novel infrared image super-resolution method via discriminative dictionary and deep residual network, which is benefited from the advantages of both compressive sensing and deep learning for more accurate reconstruction results. A discriminative dictionary learning method is introduced to explicitly learn a shared sub-dictionary with a set of cluster-specific sub-dictionaries from the training dataset to better characterize the specific representation of each image. Besides, multiple constraints are integrated to preserve the intrinsic relationship and structure information among images in the learned dictionary. Moreover, to further compensate for the limitation of dictionary representation capacity, a deep residual network is constructed to capture more high-frequency details of infrared images. Both quantitative and qualitative evaluations demonstrate that the proposed method could achieve favorable performances against other state-of-the-art methods. [ABSTRACT FROM AUTHOR]