Efficient Unified Demosaicing for Bayer and Non-Bayer Patterned Image Sensors

As the physical size of recent CMOS image sensors (CIS) gets smaller, the latest mobile cameras are adopting unique non-Bayer color filter array (CFA) patterns (e.g., Quad, Nona, QxQ), which consist of homogeneous color units with adjacent pixels. These non-Bayer sensors are superior to conventional Bayer CFA thanks to their changeable pixel-bin sizes for different light conditions but may introduce visual artifacts during demosaicing due to their inherent pixel pattern structures and sensor hardware characteristics. Previous demosaicing methods have primarily focused on Bayer CFA, necessitating distinct reconstruction methods for non-Bayer patterned CIS with various CFA modes under different lighting conditions. In this work, we propose an efficient unified demosaicing method that can be applied to both conventional Bayer RAW and various non-Bayer CFAs' RAW data in different operation modes. Our Knowledge Learning-based demosaicing model for Adaptive Patterns, namely KLAP, utilizes CFA-adaptive filters for only 1% key filters in the network for each CFA, but still manages to effectively demosaic all the CFAs, yielding comparable performance to the large-scale models. Furthermore, by employing meta-learning during inference (KLAP-M), our model is able to eliminate unknown sensor-generic artifacts in real RAW data, effectively bridging the gap between synthetic images and real sensor RAW. Our KLAP and KLAP-M methods achieved state-of-the-art demosaicing performance in both synthetic and real RAW data of Bayer and non-Bayer CFAs

YAP and TAZ maintain PROX1 expression in the developing lymphatic and lymphovenous valves in response to VEGF-C signaling

Lymphatic vasculature is an integral part of digestive, immune and circulatory systems. The homeobox transcription factor PROX1 is necessary for the development of lymphatic vessels, lymphatic valves (LVs) and lymphovenous valves (LVVs). We and others previously reported a feedback loop between PROX1 and vascular endothelial growth factor-C (VEGF-C) signaling. PROX1 promotes the expression of the VEGF-C receptor VEGFR3 in lymphatic endothelial cells (LECs). In turn, VEGF-C signaling maintains PROX1 expression in LECs. However, the mechanisms of PROX1/VEGF-C feedback loop remain poorly understood. Whether VEGF-C signaling is necessary for LV and LVV development is also unknown. Here, we report for the first time that VEGF-C signaling is necessary for valve morphogenesis. We have also discovered that the transcriptional co-activators YAP and TAZ are required to maintain PROX1 expression in LVs and LVVs in response to VEGF-C signaling. Deletion o

Overexpression of arabidopsis YUCCA6 in potato results in high-auxin developmental phenotypes and enhance

Indole-3-acetic acid (IAA), a major plant auxin, is produced in both tryptophan-dependent and tryptophanindependent pathways. A major pathway in Arabidopsis thaliana generates IAA in two reactions from tryptophan. Step one converts tryptophan to indole-3-pyruvic acid (IPA) by tryptophan aminotransferases followed by a rate-limiting step converting IPA to IAA catalyzed by YUCCA proteins. We identified eight putative StYUC (Solanum tuberosum YUCCA) genes whose deduced amino acid sequences share 50%-70% identity with those of Arabidopsis YUCCA proteins. All include canonical, conserved YUCCA sequences: FATGY motif, FMO signature sequence, and FAD-binding and NADPbinding sequences. In addition, five genes were found with ~50% amino acid sequence identity to Arabidopsis tryptophan aminotransferases. Transgenic potato (Solanum tuberosum cv. Jowon) constitutively overexpressing Arabidopsis AtYUC6 displayed high-auxin phenotypes such as narrow downward-curled leaves, increased height, erect stature, and longevity. Transgenic potato plants overexpressing AtYUC6 showed enhanced drought tolerance based on reduced water loss. The phenotype was correlated with reduced levels of reactive oxygen species in leaves. The results suggest a functional YUCCA pathway of auxin biosynthesis in potato that may be exploited to alter plant responses to the environment. © 2012 The Author

Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Network with Rotation Ensemble Module

Rotation invariance has been an important topic in computer vision tasks. Ideally, robot grasp detection should be rotation-invariant. However, rotation-invariance in robotic grasp detection has been only recently studied by using rotation anchor box that are often time-consuming and unreliable for multiple objects. In this paper, we propose a rotation ensemble module (REM) for robotic grasp detection using convolutions that rotates network weights. Our proposed REM was able to outperform current state-of-the-art methods by achieving up to 99.2% (image-wise), 98.6% (object-wise) accuracies on the Cornell dataset with real-time computation (50 frames per second). Our proposed method was also able to yield reliable grasps for multiple objects and up to 93.8% success rate for the real-time robotic grasping task with a 4-axis robot arm for small novel objects that was significantly higher than the baseline methods by 11-56%

Efficient Module Based Single Image Super Resolution for Multiple Problems

Example based single image super resolution (SR) is a fundamental task in computer vision. It is challenging, but recently, there have been significant performance improve-ments using deep learning approaches. In this article, we propose efficient module based single image SR networks (EMBSR) and tackle multiple SR problems in NTIRE 2018 SR challenge by recycling trained networks. Our proposed EMBSR allowed us to reduce training time with effectively deeper networks, to use modular ensemble for improved performance, and to separate subproblems for better per-formance. We also proposed EDSR-PP, an improved ver-sion of previous ESDR by incorporating pyramid pooling so that global as well as local context information can be utilized. Lastly, we proposed a novel denoising / deblurring residual convolutional network (DnResNet) using residual block and batch normalization. Our proposed EMBSR with DnResNet and EDSR-PP demonstrated that multiple SR problems can be tackled efficiently and effectively by win-ning the 2nd place for Track 2 (??4 SR with mild adverse condition) and the 3rd place for Track 3 (??4 SR with diffi-cult adverse condition). Our proposed method with EDSR-PP also achieved the ninth place for Track 1 (??8 SR) with the fastest run time among top nine teams