Low-level visual technology is crucial for enhancing the perception ability of unmanned aerial vehicles in complex environments. However, the coupled degradation problems such as motion blur, meteorological disturbances, and insufficient lighting in low-altitude scenarios, combined with the computational constraints of the unmanned aerial vehicle platform and the complex physical environment at high altitudes, severely restrict the robustness and 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 in adverse weather conditions, low-light enhancement, and multi-source fusion, but also systematically summarizes the existing quantitative evaluation systems. To promote the continuous evolution of low-altitude intelligent perception technology, this paper further points out that future efforts should focus on key breakthroughs in multi-modal collaboration, unsupervised/semi-supervised learning, etc., to drive the continuous advancement of low-altitude intelligent perception technology.