Multi-scale and multi-patch transformer for sandstorm image enhancement

Sandstorm is a meteorological phenomenon common in arid and semi-arid regions. A sandstorm can carry large volumes of sand unexpectedly, which leads to severe color deviations and significantly degraded visibility when an image is taken in such a scenario. However, existing image enhancement methods cannot enhance sandstorm images well due to the challenging degradations and the scarcity of sandstorm training data. In this paper, we propose a Transformer with rotary position embedding to perform sandstorm image enhancement via building multi-scale and multi-patch dependencies. Our key insights in this work are 1) a multi-scale Transformer can globally eliminate the color deviations of sandstorm images via aggregating global information, 2) a multi-patch Transformer can recover local details well via learning the spatial variant degradations, and 3) a U-shape Transformer with rotary position embedding as the core unit of multi-scale and multi-patch Transformer can effectively build the long-range dependencies. We also contribute a real-world Sandstorm Image Enhancement (SIE) dataset including 1,400 sandstorm images with different degrees of degradations and various scenes. Experiments performed on synthetic images and real-world sandstorm images demonstrate that our proposed method not only obtains visually pleasing results but also outperforms state-of-the-art methods qualitatively and quantitatively.     

高光谱成像用高帧频CMOS图像传感器北大核心

针对高帧频、全局曝光和光谱平坦等成像应用需求,设计了一款高光谱成像用CMOS图像传感器。其光敏元采用PN型光电二极管,读出电路采用5T像素结构。采用列读出电路以及高速多通道模拟信号并行读出的设计方案来获得低像素固定图像噪声(FPN)和非均匀性抑制。芯片采用ASMC 0.35μm三层金属两层多晶硅标准CMOS工艺流片,为了抑制光电二极管的光谱干涉效应,后续进行了光谱平坦化VAE特殊工艺,并对器件的光电性能进行了测试评估。电路测试结果符合理论设计预期,成像效果良好,像素具备积分可调和全局快门功能,最终实现的像素规模为512×256,像元尺寸为30μm×30μm,最大满阱电子为400 ke^(-),FPN小于0.2%,动态范围为72 dB,帧频为450 f/s,相邻10 nm波段范围内量子效率相差小于10%,可满足高光谱成像系统对CMOS成像器件的要求。     

基于改进残差特征蒸馏的轻量级超分辨率网络

基于深度学习的图像超分辨率算法通常采用递归的方式或参数共享的策略来减少网络参数，这将增加网络的深度，使得运行网络花费大量的时间，从而很难将模型部署到现实生活中。为了解决上述问题，本文设计一种轻量级超分辨率网络，对中间特征的关联性及重要性进行学习，且在重建部分结合高分辨率图像的特征信息。首先，引入层间注意力模块，通过考虑层与层之间的相关性，自适应地分配重要层次特征的权重。其次，使用增强重建模块提取高分辨率图像中更精细的特征信息，以此得到更加清晰的重建图片。通过大量的对比实验表明，本文设计的网络与其他轻量级模型相比，有更小的网络参数量，并且在重建精度和视觉效果上都有一定的提升。     

Experimental study on uplift behavior of shallow anchor plates in geogrid-reinforced soil

Geogrid reinforcement can significantly improve the uplift bearing capacity of anchor plates. However, the failure mechanism of anchor plates in reinforced soil and the contribution of geogrids need further investigation. This paper presents an experimental study on the anchor uplift behavior in geogrid-reinforced soil using particle image velocimetry (PIV) and the high-resolution optical frequency domain reflectometry (OFDR). A series of model tests were performed to identify the relationship between the failure mechanism and various factors, such as anchor embedment ratio, number of geogrid layers, and their location. The test results indicate that soil deformation and the uplift resistance of anchor plates are substantially influenced by anchor embedment ratio and location of geogrids, whereas the number of geogrid layers has limited influence. In reinforced soil, increasing the embedment ratio greatly improves the ultimate bearing capacities of anchor plates and affects the interlock between the soil and geogrids. As the embedment depth increases, the failure surfaces gradually change from a vertical slip surface to a bulb-shaped surface that is limited within the soil. The strain monitoring data shows that the deformations of geogrids are symmetrical, and the peak strains of geogrids can characterize the reinforcing effects.     

Accelerating BPC-PaCo through Visually Lossless Techniques

Fast image codecs are a current need in applications that deal with large amounts of images. Graphics Processing Units (GPUs) are suitable processors to speed up most kinds of algorithms, especially when they allow fine-grain parallelism. Bitplane Coding with Parallel Coefficient processing (BPC-PaCo) is a recently proposed algorithm for the core stage of wavelet-based image codecs tailored for the highly parallel architectures of GPUs. This algorithm provides complexity scalability to allow faster execution at the expense of coding efficiency. Its main drawback is that the speedup and loss in image quality is controlled only roughly, resulting in visible distortion at low and medium rates. This paper addresses this issue by integrating techniques of visually lossless coding into BPC-PaCo. The resulting method minimizes the visual distortion introduced in the compressed file, obtaining higher-quality images to a human observer. Experimental results also indicate 12% speedups with respect to BPC-PaCo.     

Green Measurements for Software Product Based on Sustainability Dimensions

Software is a central component in the modern world and vastly affects the environment’s sustainability. The demand for energy and resource requirements is rising when producing hardware and software units. Literature study reveals that many studies focused on green hardware; however, limited efforts were made in the greenness of software products. Green software products are necessary to solve the issues and problems related to the long-term use of software, especially from a sustainability perspective. Without a proper mechanism for measuring the greenness of a particular software product executed in a specific environment, the mentioned benefits will not be attained. Currently, there are not enough works to address this problem, and the green status of software products is uncertain and unsure. This paper aims to identify the green measurements based on sustainable dimensions in a software product. The second objective is to reveal the relationships between the elements and measurements through empirical study. The study is conducted in two phases. The first phase is the theoretical phase, where the main components, measurements and practices that influence the sustainability of a software product are identified. The second phase is the empirical study that involved 103 respondents in Malaysia investigating current practices of green software in the industrial environment and further identifying the main sustainability dimensions and measurements and their impact on achieving green software products. This study has revealed seven green measurements of software product: Productivity, Usability, Cost Reduction, Employee Support, Energy Efficiency, Resource Efficiency and Tool Support. The relationships are statistically significant, with a significance level of less than 0.01 (p = 0.000). Thus, the hypothesised relationships were all accepted. The contributions of this study revolve around the research perspectives of the measurements to attain a green software product.     

样品前处理QuEChERS法及新型吸附材料在水产品中抗生素残留分析中的应用进展

抗生素的不合理使用，给水产品带来了一系列的潜在食用安全风险。品种繁多且残留痕量的抗生素也为食品安全检测带来了新的挑战，发展准确、快速、简便、高效且安全的前处理方法尤为重要。QuEChERS法作为一种新型样品前处理技术，现已逐渐应用到水产品抗生素残留检测中，并可实现较好的回收率。由于水产品中蛋白质和脂类含量高，样品基质复杂，使用经典QuEChERS法无法满足抗生素分析需求，改良其提取方法和净化方法是保证分析结果的关键。本文综述QuEChERS法原理特点，总结归纳其在针对水产品抗生素分析上的主要方法改良手段，并就近十年来改良QuEChERS法在水产品抗生素残留分析中的应用进行文献归纳，同时介绍新型吸附材料在该领域的使用，为探索新的净化方式提供参考，以期帮助检测人员持续优化改进QuEChERS法，推动和扩大QuEChERS法在水产品抗生素残留分析中的应用范围。     

基于现代审美下的赣傩艺术元素分析与设计应用

目的 赣傩艺术元素内容丰富并保存完整,是研究我国古代赣傩地区文化与艺术的珍贵史料.研究赣傩艺术元素的提取及衍生图案的设计方法,在设计应用中表达赣傩傩面具、选取傩舞形和构筑傩庙魂,能给当代赣傩艺术元素的产品设计以指引.方法 通过直接引用、形意结合和局部强化的设计方法,增厚其丰富内涵、设计个性和主题意蕴.通过现代化审美中"绿色化、感性化、地域化"的要求,遵循"以退为进,化繁为简""多元并存,个性体现"的原则,强化赣傩艺术元素的独特风格.结论 用现代的手法,将特色的赣傩艺术元素植入到理性的产品设计中去,使赣傩艺术元素与现代设计思想有机结合.在继承传统文化与现代流行中找到契合点,深入挖掘赣傩艺术元素蕴含的文化精神、文化胸怀和文化自信,使中华传统文化在现代审美下进行创新再现.     

Quality assessment for color correction-based stitched images via bi-directional matching

With the deepening of social information, the panoramic image has drawn a significant interest of viewers and researchers as it can provide a very wide field of view (FoV). Since panoramic images are usually obtained by capturing images with the overlapping regions and then stitching them together, image stitching plays an important role in generating panoramic images. In order to effectively evaluate the quality of stitched images, a novel quality assessment method based on bi-directional matching is proposed for stitched images. Specifically, dense correspondences between the testing and benchmark stitched images are first established by bi-directional SIFT-flow matching. Then, color-aware, geometric-aware and structure-aware features are respectively extracted and fused via support vector regression (SVR) to obtain the final quality score. Experiments on our newly constructed database and ISIQA database demonstrate that the proposed method can achieve comparable performance compared with the conventional blind quality metrics and the quality metrics specially designed for stitched images.