基于引导滤波的无人机影像增强在铁路勘察设计中的应用OA

UAV remote sensing offers numerous advantages such as timeliness,mobility,safety,high resolution,and low cost.However,due to the poor attitude stability of UAV platforms and the influence of weather conditions,UAV remote sensing images commonly suffer from degraded contrast and clarity,making them difficult to directly apply in railway survey and design.To address this widespread degradation in UAV remote sensing images,this paper proposes a fast and effective image enhancement method based on guided filtering combined with histogram matching.The algorithm first corrects image grayscale through block-wise adaptive histogram matching,expanding the dynamic range to enhance contrast and amplify subtle grayscale variations.Subsequently,it applies a guided filter with noise reduction and edge preservation properties to the corrected image based on a local linear model,separating fine details from background noise.Finally,the guided map and filtered map are linearly recombined,with increased weighting applied to the detail layer to produce the enhanced image.Experimental results demonstrate that this method significantly enhances image texture details while reducing noise,outperforming the classic Wallis method in both contrast and information entropy metrics.When applied to image matching,it increases the number of correctly matched points by nearly 1.5 times without compromising matching point accuracy.UAV images optimized by this algorithm can be effectively applied to railway survey and design,offering high practical value.