Laser remote sensing images are affected by environmental factors such as atmospheric conditions and the reflection characteristics of the target surface, resulting in blurring or loss of details in the images. To obtain high-precision single frame feature reconstruction results, a single frame feature reconstruction method in laser remote sensing images is studied. By calculating local correlation coefficients and fourth-order correlation coefficients in the low-frequency sub bands of laser remote sensing images, replacing the low-frequency coefficients of the original image with high-frequency coefficients, and performing non downsampling contour inverse transformation, the panchromatic sharpening of laser remote sensing images is achieved. The K-means clustering algorithm is used to group the image into bands, and independent reconstruction and iterative correction are performed. The reconstruction values of non reference bands are obtained through weighted average fusion. The experimental results show that the reconstruction accuracy of this method reaches above 0.95 and significantly improves the image detail effect.