Sichuan mountainous environment and urban planning based on image dehazing algorithm

Currently, image defogging algorithms can be divided into traditional defogging algorithms and defogging algorithms based on deep learning. Traditional defogging algorithms generally use image enhancement methods to enhance the contrast of foggy images, or use a priori theory to estimate the transmittance value and atmospheric illumination value of the foggy image, and then restore the fogless image through the atmospheric scattering model. The defogging algorithm based on deep learning is generally based on the prior information of the foggy image to the model, and the supervised learning or unsupervised learning is used to estimate the transmittance map or non-haze map of the foggy image, which has strong generalization. The calculation efficiency is higher. At present, the research on the ecological element theory of mountainous cities in Sichuan includes two parts. One is related to basic theories, mainly about the development of ecological theory, and related research on the basic principles of urban ecology and environmental science. The second is to consider the research in southwestern China. Practical issues are researched on the concept of ecological elements in southwest mountain cities. Urban ecological space is the hardware environment and space foundation of the urban ecological system. The research and development of ecological elements and the composition and basic functional characteristics of ecological elements are explained. The urban ecological space structure is the overall framework of the urban ecological space, the organic integration of various components of the ecological space, and the expression of the time and space of the ecological system within the physical elements of the space. And as a whole, in order to maintain an ecologically balanced system, the urban ecological space service function takes natural biological production as its basic attribute.