Super-resolution imaging for infrared micro-scanning optical system

Low-Light-Level Image Super-Resolution Reconstruction Based on a Multi-Scale Features Extraction Network

Wide field-of-view (FOV) and high-resolution (HR) imaging are essential to many applications where high-content image acquisition is necessary. However, due to the insufficient spatial sampling of the image detector and the trade-off between pixel size and photosensitivity, the ability of current imaging sensors to obtain high spatial resolution is limited, especially under low-light-level (LLL) imaging conditions. To solve these problems, we propose a multi-scale feature extraction (MSFE) network to realize pixel-super-resolved LLL imaging. In order to perform data fusion and information extraction for low resolution (LR) images, the network extracts high-frequency detail information from different dimensions by combining the channel attention mechanism module and skip connection module. In this way, the calculation of the high-frequency components can receive greater attention. Compared with other networks, the peak signal-to-noise ratio of the reconstructed image was increased by 1.67 dB. Extensions of the MSFE network are investigated for scene-based color mapping of the gray image. Most of the color information could be recovered, and the similarity with the real image reached 0.728. The qualitative and quantitative experimental results show that the proposed method achieved superior performance in image fidelity and detail enhancement over the state-of-the-art.

High-Efficiency Microsatellite-Using Super-Resolution Algorithm Based on the Multi-Modality Super-CMOS Sensor

To realize the application of super-resolution technology from theory to practice, and to improve microsatellite spatial resolution, we propose a special super-resolution algorithm based on the multi-modality super-CMOS sensor which can adapt to the limited operation capacity of microsatellite computers. First, we designed an oblique sampling mode with the sensor rotated at an angle of 26.56∘(arctan12) to obtain high overlap ratio images with sub-pixel displacement. Secondly, the proposed super-resolution algorithm was applied to reconstruct the final high-resolution image. Because the satellite equipped with this sensor is scheduled to be launched this year, we also designed the simulation mode of conventional sampling and the oblique sampling of the sensor to obtain the comparison and experimental data. Lastly, we evaluated the super-resolution quality of images, the effectiveness, the practicality, and the efficiency of the algorithm. The results of the experiments showed that the satellite-using super-resolution algorithm combined with multi-modality super-CMOS sensor oblique-mode sampling can increase the spatial resolution of an image by about 2 times. The algorithm is simple and highly efficient, and can realize the super-resolution reconstruction of two remote-sensing images within 0.713 s, which has good performance on the microsatellite.