19 research outputs found
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
Fully Quantized Always-on Face Detector Considering Mobile Image Sensors
Despite significant research on lightweight deep neural networks (DNNs)
designed for edge devices, the current face detectors do not fully meet the
requirements for "intelligent" CMOS image sensors (iCISs) integrated with
embedded DNNs. These sensors are essential in various practical applications,
such as energy-efficient mobile phones and surveillance systems with always-on
capabilities. One noteworthy limitation is the absence of suitable face
detectors for the always-on scenario, a crucial aspect of image sensor-level
applications. These detectors must operate directly with sensor RAW data before
the image signal processor (ISP) takes over. This gap poses a significant
challenge in achieving optimal performance in such scenarios. Further research
and development are necessary to bridge this gap and fully leverage the
potential of iCIS applications. In this study, we aim to bridge the gap by
exploring extremely low-bit lightweight face detectors, focusing on the
always-on face detection scenario for mobile image sensor applications. To
achieve this, our proposed model utilizes sensor-aware synthetic RAW inputs,
simulating always-on face detection processed "before" the ISP chain. Our
approach employs ternary (-1, 0, 1) weights for potential implementations in
image sensors, resulting in a relatively simple network architecture with
shallow layers and extremely low-bitwidth. Our method demonstrates reasonable
face detection performance and excellent efficiency in simulation studies,
offering promising possibilities for practical always-on face detectors in
real-world applications.Comment: Accepted to ICCV 2023 Workshop on Low-Bit Quantized Neural Networks
(LBQNN), Ora
Comparison of online social relations in volume vs interaction
Online social networking services are among the most popular Internet services according to Alexa.com and have become a key feature in many Internet services. Users interact through various features of online social networking services: making friend relationships, sharing their photos, and writing comments. These friend relationships are expected to become a key to many other features in web services, such as recommendation engines, security measures, online search, and personalization issues. However, we have very limited knowledge on how much interaction actually takes place over friend relationships declared online. A friend relationship only marks the beginning of online interaction.
Does the interaction between users follow the declaration of friend relationship? Does a user interact evenly or lopsidedly with friends? We venture to answer these questions in this work. We construct a network from comments written in guestbooks. A node represents a user and a directed edge a comments from a user to another. We call this network an activity network. Previous work on activity networks include phone-call networks [34, 35] and MSN messenger networks [27]. To our best knowledge, this is the first attempt to compare the explicit friend relationship network and implicit activity network.
We have analyzed structural characteristics of the activity network and compared them with the friends network. Though the activity network is weighted and directed, its structure is similar to the friend relationship network. We report that the in-degree and out-degree distributions are close to each other and the social interaction through the guestbook is highly reciprocated. When we consider only those links in the activity network that are reciprocated, the degree correlation distribution exhibits much more pronounced assortativity than the friends network and places it close to known social networks. The k-core analysis gives yet another corroborating evidence that the friends network deviates from the known social network and has an unusually large number of highly connected cores.
We have delved into the weighted and directed nature of the activity network, and investigated the reciprocity, disparity, and network motifs. We also have observed that peer pressure to stay active online stops building up beyond a certain number of friends.
The activity network has shown topological characteristics similar to the friends network, but thanks to its directed and weighted nature, it has allowed us more in-depth analysis of user interaction