Thin-film diffractive optical elements have considerable potential to be used in the field of high-resolution remote sensing imaging satellites because of advantages such as a large aperture, small volume, lightness, wide tolerance range of surface shape, and easy replication. However, there are problems associated with thin-film diffraction imaging, including space variation, serious blur, and low contrast, which result in insufficient imaging quality with regard to traditional optical system requirements. To address this, a local adaptive prior-based image restoration method is proposed for thin-film diffraction imaging systems. An entire degraded image was divided into several isohalo regions based on imaging characteristics. Then, the regularization constraints were adaptively selected and updated according to the local scene prior characteristics. Additionally, the system parameters in the corresponding field of view were used as input to restore each subregion. In particular, the diffraction efficiency was introduced into the model to remove the non-design level background radiation. The experimental results show that the proposed algorithm can effectively improve the image quality of a thin-film diffraction imaging system, including space variation correction, clarity enhancement, and background radiation suppression. Furthermore, a diffraction efficiency of less than 60% was found to significantly impact the final image products.

Diffractive membrane imaging systems have been an important development trend for high-orbit satellite cameras owing to their advantages of large aperture, light weight, rapid manufacture, and low cost. However, caused by the cross-coupling effects of diffraction imaging, membrane properties, subaperture stitching, on-orbit disturbances, and other physical factors, lager-aperture space diffractive membrane imaging systems have specific and complex degradation characteristics: the modulation transfer function (MTF) and signal-to-noise ratio (SNR) have more prominent degradation and serious space-variant characteristics over fields of view, with obvious background radiation properties that seriously affect the application of imaging products. To address this problem, this study established a global information transmission model by characterizing the PSF and background radiation in a full field of view to represent the imaging law of an on-orbit system. Aiming at the inverse problem of the information transmission model, we also propose a novel image inversion restoration method for the special degradation characteristics. In particular, the effect of diffraction efficiency is introduced into the inversion restoration method to solve the background radiation problem. Moreover, we innovatively designed matrix regularization parameters to further improve the correction ability of spatial variation. When the diffraction efficiency was experimentally higher than 60% and the mean measured spatial variability was less than 0.2, the proposed method exhibited a satisfactory processing performance, and could improve multiobjective comprehensive processing, such as transfer function compensation, spatial variation correction, and background radiation removal.

Ship detection in optical remote sensing images plays a vital role in numerous civil and military applications, encompassing maritime rescue, port management and sea area surveillance. However, the multi-scale and deformation characteristics of ships in remote sensing images, as well as complex scene interferences such as varying degrees of clouds, obvious shadows, and complex port facilities, pose challenges for ship detection performance. To address these problems, we propose a novel ship detection method by combining multi-scale deformation modeling and fine region highlight-based loss function. First, a visual saliency extraction network based on multiple receptive field and deformable convolution is proposed, which employs multiple receptive fields to mine the difference between the target and the background, and accurately extracts the complete features of the target through deformable convolution, thus improving the ability to distinguish the target from the complex background. Then, a customized loss function for the fine target region highlight is employed, which comprehensively considers the brightness, contrast and structural characteristics of ship targets, thus improving the classification performance in complex scenes with interferences. The experimental results on a high-quality ship dataset indicate that our method realizes state-of-the-art performance compared to eleven considered detection models.