The greenhouse gas emission during its materialization stage is characterized by time-concentrated and large emissions. This study first divided the materialization stage into three segments according to the life cycle theory: building materials production, building materials transportation, and construction. A carbon emission measurement model for the materialization stage of large public buildings was established by integrating the carbon emission coefficient method, and the emission reduction rate (ERR) was proposed as an indicator for quantitatively assessing carbon emissions during the materialization stage of large public buildings. After the initial indicator system was framed, the Analytic Network Process and Fuzzy Comprehensive Evaluation methods were used to establish the evaluation model for large public buildings at the materialization stage. Last, the model was verified at a convention and exhibition center in Xiamen, China, and it shows that the production stage of building materials has a large potential for emission reduction.

Histogram equalization (HE) is the most famous method for image enhancement due to its low computation complexity and wide application scope. However, existing HE-based methods often lead to over-enhancement, under-enhancement and unnatural visual perception. To overcome these defects, an adaptive HE algorithm is proposed in this paper. The core idea is to adjust the histogram with the optimal gamma correction parameter. Firstly, a sequence of images with gradually changing quality is obtained by traversing the gamma parameters, and two measurement values are calculated for each image in the sequence. Then a binary optimization model is proposed to search for the optimal parameter. Importantly, in order to compensate for the difference between the objective model and the subjective perception, a novel correction factor is designed to adjust the optimal parameter from the model. Finally, secondary gamma correction is performed by inverting the final parameter to preserve image details and prevent under enhancement. Experimental results show that the proposed algorithm outperforms those state-of-the-art HE-based algorithms.

针对传统直方图均衡算法的不足,如增强过度,均值漂移和细节丢失等,本文提出了一种基于图像序列分析的全局直方图均衡算法.受到CLAHE的启发,本文设计了两种数学模型.图像渐变模型将输入的单张图像输出为质量渐变的图像序列;再根据序列图像的统计指标,经过混合优化模型计算出最优控制参数.为了提升主观感知效果本文提出了一种直方图后处理方法,最后根据修正后的直方图进行均衡化处理.实验结果表明:本文算法的结果图像具有较好的主观感受度和客观评价值,并且优于近年来提出的直方图均衡改进算法.

Since more and more outdoor images are often degraded by haze and suffer from bad visibility, haze removal has become an important task of image restoration in recent decades. A systematic dehazing framework based on Koschmieder model is proposed in this paper, which adopts a novel brightness-area suppression mechanism. Firstly, global brightness-area suppression blending the large-scale atmospheric veil with the result of edge-preserving filtering, could protect the white objects not becoming darker. Then, the local brightness-area suppression based on sky detection could prevent the sky region from over saturation. In addition, post-processing procedures are designed in this dehazing system in order to generate haze-free image with better visual perception. This framework is on-limits and extensible, in that, it can accept other better dehazing technique as one of the core steps inside. Experiments show that the performance of this framework outperforms multiple state-of-the-art dehazing algorithms.