The Mars Color Camera (MCC) of Indian Space Research Organisation (ISRO) captures the elusive moon of Mars, Phobos. MCC instrument is the eye of Mars Orbiter Mission (MOM) launched by ISRO on 05^th November 2013 from the spaceport of India, Sriharikota. MCC has already provided more than 1000 images of Martian surface features covering different terrains. Recently, MCC has captured Phobos image at successive time instances on 01^st July 2020. Multiple unpaired acquisitions of planetary remote sensing images often contain rich information to super resolve the spatial details of these images. This paper describes the techniques developed to enhance the Phobos image from MCC multi-frame acquisitions using image rectification and topographic data. In this regard, the co-registration of MCC frames is a crucial step. To co-register the data at sub-pixel level, we present a method, namely Random Sample Consensus (RANSAC) pruning based Scale Invariant Feature Transform (R-SIFT). The image composite is based on Medoid approach. It is further enhanced by contrast limited adaptive histogram equalization (CLAHE). The location of Phobos is computed using Spacecraft Planet Instrument Camera Matrix Event (SPICE) toolkit. For topographic correction, we rely on the Digital Elevation Model (DEM) of Phobos using publicly available resources. Visual inspection and image quality parameters, such as BRISQUE, RMSE, PSNR, and SSIM are estimated for both enhanced and topographically corrected image for assimilation purpose. After incorporating these techniques, the final Phobos image appears more representative, spatially enhanced, and has normalized radiometry to study its surface features.

印度太空研究组织（ISRO）的火星彩色相机（MCC）拍摄了火卫一火星难以捉摸的卫星。MCC仪器是ISRO于2013年11月5^日从印度Sriharikota太空港发射的火星轨道飞行器（MOM）的视线。MCC已经提供了1000幅覆盖不同地形的火星表面特征图像。最近，我的客户中心（MCC）在1月1^日的连续时间捕获了Phobos图像2020年7月。多次不成对采集的行星遥感图像通常包含丰富的信息，以超级解析这些图像的空间细节。本文介绍了使用图像校正和地形数据从MCC多帧采集中增强Phobos图像的技术。在这方面，MCC帧的共注册是至关重要的一步。为了在子像素级上共注册数据，我们提出了一种方法，即基于随机样本共识（RANSAC）修剪的尺度不变特征变换（R-SIFT）。图像合成基于Medoid方法。对比度受限的自适应直方图均衡化（CLAHE）进一步增强了该功能。火卫一的位置是使用航天器行星仪器相机矩阵事件（SPICE）工具包计算的。对于地形校正，我们使用公开资源来依赖Phobos的数字高程模型（DEM）。视觉检查和图像质量参数（例如BRISQUE，RMSE，PSNR和SSIM）被估计用于增强图像和经地形校正的图像，以达到同化目的。整合了这些技术之后，最终的Phobos图像看起来更具代表性，在空间上得到增强，并具有标准化的辐射度法来研究其表面特征。