Ryodo Hemmi

<jats:title>Abstract</jats:title> <jats:p> High-quality digital terrain models (DTMs) are essential for lunar polar missions, aiding mission planning and surface operations. The horizontal resolution and vertical accuracy of DTMs are generally limited by factors such as source image resolution, image noise, the precision of ground control points (GCPs), and data processing techniques. In this study, we produced advanced DTMs for the lunar south pole region by integrating seamless mosaics generated from multiple Lunar Reconnaissance Orbiter Camera Narrow Angle Camera image pairs captured under various illumination azimuths. To enhance accuracy and reduce artifacts, we employed bundle adjustment with hundreds of meticulously selected image-to-image tie points and precise GCPs, combined with an advanced multiview shape-from-shading technique. This technique, which utilizes multiple viewpoints to resolve topographic details with higher precision, significantly improves vertical accuracy and resolution. Our methodology achieves a spatial resolution of 1 m pixel <jats:sup>−1</jats:sup> and vertical precision of ±1.0 m, enabling clearer delineation of meter-scale lunar topographic features compared to previous models. These DTMs are expected to significantly support the Lunar Polar Exploration project and other forthcoming lunar exploration missions. </jats:p>

<jats:p>We present an automated and fully reproducible pipeline for restoring motion-smeared Mars Express SRC images of Phobos. A one-dimensional motion point spread function (PSF) is derived directly from SPICE geometry and microsecond-precision exposure timing, and Wiener deconvolution (SNR = 16 dB) is applied to recover image sharpness. Tested on 14 images from 4 orbits spanning slant distances of 52–292 km, exposures of 14–20 milliseconds, sampling of 0.47–2.7 m/pixel, and PSF lengths of 11–119 pixels, the method achieves up to 31.7 dB PSNR, 0.78 SSIM, and positive sharpness gains across all cases. The restored images reveal sub-meter surface features previously obscured by motion blur, with residual energy reduced relative to the acquisition model. The workflow relies solely on open data and open-source tools (ISIS, ALE/SpiceyPy, OpenCV), requires no star-field calibration, and generalizes to other motion-degraded planetary datasets, providing a fully transparent and reproducible solution for high-resolution planetary imaging.</jats:p>