Low-level visual technology is crucial for enhancing the perception capability of unmanned aerial vehicles (UAV) in complex environments. However, the coupled degradation problems unique to low-altitude scenarios, such as motion blur, meteorological disturbances, and insufficient illumination, combined with the computational constraints of UAV platforms and the complex physical environment at low altitudes, severely restrict the robustness and edge-side real-time performance of existing algorithms. To address this, this paper systematically reviews the research progress in the low-altitude low-level vision field, focusing on three core directions: degradation recovery, information enhancement, and quality assessment. This paper not only deeply analyzes the technical characteristics and application value of cutting-edge methods such as super-resolution, degradation removal under adverse weather conditions, low-light enhancement, and multi-source fusion, but also systematically summarizes the existing quantitative evaluation systems. Furthermore, this paper points out that future efforts should focus on key breakthroughs in multimodal collaboration, unsupervised/self-supervised learning, etc., to drive the continuous advancement of low-altitude intelligent perception technology.