Space smart optical orbiting payloads integrated with attitude and position (SSPIAP) are emerging as an essential tool that is extensively used in microsatellites. The on-orbit imaging link of SSPIAPs includes atmospheric disturbances, defocusing, and relative motion, and other noises, thereby resulting in low modulation transfer function (MTF) and poor image quality. The introduction of MTF compensations has pushed the limits of optical imaging, enabling high-resolution on-orbit dynamic imaging. However, the external targets for compensating MTF are limited by space and time because the availability and access to external targets are infrequently easy when a remote sensor is working on-orbit. Here, a new and robust MTF self-compensation method for a SSPIAP is proposed. In comparison with conventional methods with external targets, this method utilizes multiple natural sub-resolution features (SRFs), occupying several pixels on a uniform background, as observation targets which makes MTFC more maneuverable, robust and authentic. A mathematical morphology algorithm is used to extract SRFs. Moreover, the method relies on a regularization total variation energy function, a sparse prior framework, to invert the MTF. Experimental measurements confirm that the proposed method is effective and convenient to implement. This technique does not rely on specific external targets to compensate the MTF, making it potentially suitable for on-orbit dynamic long-range imaging.
In saliency-based compressive sampling (CS) for remote sensing image signals, the saliency information of images is used to allocate more sensing resources to salient regions than to non-salient regions. However, the pulsed cosine transform method can generate large errors in the calculation of saliency information because it uses only the signs of the coefficients of the discrete cosine transform for low-resolution images. In addition, the reconstructed images can exhibit blocking effects because blocks are used as the processing units in CS. In this work, we propose a post-transform frequency saliency CS method that utilizes transformed post-wavelet coefficients to calculate the frequency saliency information of images in the post-wavelet domain. Specifically, the wavelet coefficients are treated as the pixels of a block-wise megapixel sensor. Experiments indicate that the proposed method yields better-quality images and outperforms conventional saliency-based methods in three aspects: peak signal-to-noise ratio, mean structural similarity index, and visual information fidelity.
Super-black materials refer to the materials whose reflectivity is less than 0.5% and have many significant applications in the aerospace field. Though super-black materials have excellent optical properties, most of them cannot be directly applied to the baffle of star trackers because of all kinds of limits. We presented a method to improve the preparation of superblack materials based on Ni-P alloys which helped this material better applied to star tracker baffle. This process chose the concentration of 10% oxalic acid solution to activate the aluminium surface and enlarged the plating area of Ni-P alloys film. Meanwhile, we anodised Ni-P alloys film in the non-oxidising acid solution instead of conventional blackening process. Consequently, the method improved the controllability of blackening process and avoided the time error. Thus, this method of preparing super-black materials could be applied to star tracker baffle.
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