Due to the presence of turbid media, such as microdust and water vapor in the environment, outdoor pictures taken under hazy weather circumstances are typically degraded. To enhance the quality of such images, this wo...Due to the presence of turbid media, such as microdust and water vapor in the environment, outdoor pictures taken under hazy weather circumstances are typically degraded. To enhance the quality of such images, this work proposes a new hybrid λ2-λ0 penalty model for image dehazing. This model performs a weighted fusion of two distinct transmission maps, generated by imposing λ2 and λ0 norm penalties on the approximate regression coefficients of the transmission map. This approach effectively balances the sparsity and smoothness associated with the λ0 and λ2 norms, thereby optimizing the transmittance map. Specifically, when the λ2 norm is penalized in the model, an updated guided image is obtained after implementing λ0 penalty. The resulting optimization problem is effectively solved using the least square method and the alternating direction algorithm. The dehazing framework combines the advantages of λ2 and λ0 norms, enhancing sparse and smoothness, resulting in higher quality images with clearer details and preserved edges.展开更多
Web 2.0的出现使信息构建(IA)的内容发生了深刻变化,IA已进入"信息构建2.0"(IA2.0)阶段。在IA2.0阶段,IA作为一门学科、一种角色和一类社团的协调统一体而存在,它强调真正"以用户为中心"和"丰富的用户体验&qu...Web 2.0的出现使信息构建(IA)的内容发生了深刻变化,IA已进入"信息构建2.0"(IA2.0)阶段。在IA2.0阶段,IA作为一门学科、一种角色和一类社团的协调统一体而存在,它强调真正"以用户为中心"和"丰富的用户体验"的核心理念,以满足新环境下的用户需求。Web2.0网站的IA,是IA2.0的典型应用,也是IA2.0阶段研究的主要内容,本文将其称为网站IA2.0。文中设计了一个网站IA2.0模型,并进行了简单的实例分析。展开更多
文摘Due to the presence of turbid media, such as microdust and water vapor in the environment, outdoor pictures taken under hazy weather circumstances are typically degraded. To enhance the quality of such images, this work proposes a new hybrid λ2-λ0 penalty model for image dehazing. This model performs a weighted fusion of two distinct transmission maps, generated by imposing λ2 and λ0 norm penalties on the approximate regression coefficients of the transmission map. This approach effectively balances the sparsity and smoothness associated with the λ0 and λ2 norms, thereby optimizing the transmittance map. Specifically, when the λ2 norm is penalized in the model, an updated guided image is obtained after implementing λ0 penalty. The resulting optimization problem is effectively solved using the least square method and the alternating direction algorithm. The dehazing framework combines the advantages of λ2 and λ0 norms, enhancing sparse and smoothness, resulting in higher quality images with clearer details and preserved edges.
