Pointing error is a critical performance metric for vehicle-mounted single-photon ranging theodolites (VSRTs). Achieving high-precision pointing through processing and adjustment can incur significant costs. In this study, we propose a cost-effective digital correction method based on a piecewise linear regression model to mitigate this issue. Firstly, we introduce the structure of a VSRT and conduct a comprehensive analysis of the factors influencing its pointing error. Subsequently, we develop a physically meaningful piecewise linear regression model that is both physically meaningful and capable of accurately estimating the pointing error. We then calculate and evaluate the regression equation to ensure its effectiveness. Finally, we successfully apply the proposed method to correct the pointing error. The efficacy of our approach has been substantiated through dynamic accuracy testing of a 450 mm optical aperture VSRT. The findings illustrate that our regression model diminishes the root mean square (RMS) value of VSRT’s pointing error from 17″ to below 5″. Following correction utilizing this regression model, the pointing error of VSRT can be notably enhanced to the arc-second precision level.

In order to improve the low efficient and the noise effect of remote sensing image reconstruction, an algorithm of adaptive dual gradient projection for sparse reconstruction of compressed sensing theory is proposed. Point to the high frequency noise of remote sensing image, the ADGPSR algorithm pursuits the projection direction in two conjudate directions, thus the high frequency noise effect is overcame. Experiment results show that, compared with the GPSR algorithm, the ADGPSR algorithm on remote sensing image improves the signals reconstruction accuracy.

In order to realize the rapid H.265 codec method, a new H.265 codec method based on Compressed Sensing Theory is proposed. The H.265 codec method are divided into three different types, intra-frame type, interframe type and mix frame type. Combined with the Compressed Sensing Theory, the input images linearly transform into the residual data, scale and quantify the transformed coefficients; after loop filtering and adaptive compensation, the H.265 coding data are got. When decoding, the process is reversed. Experimental results show that this method improves the image video codec quality and the image video codec speeds.