Image Visual Reconstruction Method for Landscape Space Environment Design

To improve garden landscape spatial environment design effect, a sparse Bayesian algorithm based image visual reconstruction method is proposed to construct an optimized image visual reconstruction model. It is based on garden landscape spatial environment design. In the model construction, gray pixel feature separation method and multi-level feature decomposition method are combined for image visual feature reconstruction. The experimental results show that the minimum error value of the optimized sparse Bayesian algorithm is 0.080, the minimum running time is 264.5 s, and the maximum average PSNR and SSIM values are 25.941 ㏈ and 0.715, respectively. The minimum resolution of the sparse Bayesian visual image reconstruction method is 408, the minimum error value is 0.0505, the maximum value and standard deviation are 0.123 and 0.0261, respectively. The average structural similarity of the sparse Bayesian image visual reconstruction method in 50 experiments is about 3.54. It improves by 54.6% and 88.3% respectively compared to convolutional neural based image reconstruction methods and non local variation based image reconstruction methods. The above results show that the image visual reconstruction method for landscape spatial environment design has certain application value in the field of landscape design.