3 research outputs found

    Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report

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    The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper

    On conservation of world heritage Beijing-Hangzhou grand canal for enhancing cultural ecosystem services

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    Abstract The Beijing-Hangzhou Grand Canal carries unique social and cultural significance as a world cultural heritage, but with the acceleration of global urbanization, it has potentially severe environmental risks under continuous anthropogenic disturbances. Therefore, to protect the ecological and cultural values of the Grand Canal, it is necessary to assess the corresponding relationship of water quality to land use and the perception of ecosystem services that focus on cultural ecosystem services (CES). This study aims to analyze the water quality response to land use in the Beijing-Hangzhou Grand Canal, describe the land use types closely related to water quality, and propose corresponding management strategies for enhancing CES. This study investigated the impacts of land use structure and landscape pattern on water quality by calculating the correlation between land use structure and landscape pattern indices and water quality in buffer zones of different distances on both sides of the canal. The results show that green land dominates the land use structure and can effectively reduce water pollution in the canal. On the other hand, urban impervious surfaces showed a significant positive correlation with pollution contributing to low water quality. We accessed the impact of water quality on the perception of CES in the Beijing-Hangzhou Grand Canal and proposed optimization strategies for promoting CES. Both content analysis and thematic analysis were applied to analyze the impact of the water environment quality of the Beijing-Hangzhou Grand Canal on the perception of CES. We found that the perceptions of CES along the Beijing-Hangzhou Grand Canal are associated with the public’s opinions on its cultural heritage services and artistic inspiration services. The perceptions of CES are closely related to the quality of the water environment and riparian greenness, which affect the values of cultural heritage and conservation of the Beijing-Hangzhou Grand Canal
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