The extraction effect is paramount in enhancing the clarity of Ronchi imagery. To this end, we introduce a wavelet-based method for processing large-aperture aspheric mirror pictures. This approach to image optimization significantly enhances the information content, rendering images more conducive to detection and recognition by human eyes. Unfortunately, current optimization techniques often fall short, lacking precise quantitative metrics and, consequently, failing to yield images optimal for human detection and recognition. In some cases, even the detectable and recognizable scene information within the optimized image may be diminished. Traditional approaches to building image extraction and optimization primarily rely on adjustments to segmentation algorithms and post-filtering optimization processes, yet these methods are insufficient in addressing the challenge of irregular building edges. Our research, therefore, builds upon the foundation of virtual reality technology, necessitating the establishment of a multidimensional information space. Virtual reality will serve as the cornerstone in supporting this intricate information landscape.