Background Owing to the rapid development of deep networks, single-image deraining tasks have progressed significantly. Various architectures have been designed to recursively or directly remove rain, and most rain st...Background Owing to the rapid development of deep networks, single-image deraining tasks have progressed significantly. Various architectures have been designed to recursively or directly remove rain, and most rain streaks can be removed using existing deraining methods. However, many of them cause detail loss, resulting in visual artifacts. Method To resolve this issue, we propose a novel unrolling rain-guided detail recovery network(URDRN) for single-image deraining based on the observation that the most degraded areas of a background image tend to be the most rain-corrupted regions. Furthermore, to address the problem that most existing deep-learningbased methods trivialize the observation model and simply learn end-to-end mapping, the proposed URDRN unrolls a single-image deraining task into two subproblems: rain extraction and detail recovery. Result Specifically, first, a context aggregation attention network is introduced to effectively extract rain streaks;thereafter, a rain attention map is generated as an indicator to guide the detail recovery process. For the detail recovery sub-network, with the guidance of the rain attention map, a simple encoder–decoder model is sufficient to recover the lost details.Experiments on several well-known benchmark datasets show that the proposed approach can achieve performance similar to those of other state-of-the-art methods.展开更多
Qinghai-Tibet Plateau lakes are important carriers of water resources in the‘Asian’s Water Tower’,and it is of great significance to grasp the spatial distribution of plateau lakes for the climate,ecological enviro...Qinghai-Tibet Plateau lakes are important carriers of water resources in the‘Asian’s Water Tower’,and it is of great significance to grasp the spatial distribution of plateau lakes for the climate,ecological environment,and regional water cycle.However,the differences in spatial-spectral characteristics of various types of plateau lakes,and the complex background information of plateau both influence the extraction effect of lakes.Therefore,it is a great challenge to completely and effectively extract plateau lakes.In this study,we proposed a multiscale contextual information aggregation network,termed MSCANet,to automatically extract Plateau lake regions.It consists of three main components:a multiscale lake feature encoder,a feature decoder,and a Multicore Pyramid Pooling Module(MPPM).The multiscale lake feature encoder suppressed noise interference to capture multiscale spatial-spectral information from heterogeneous scenes.The MPPM module aggregated the contextual information of various lakes globally.We applied the MSCANet to the lake extraction of the Qinghai-Tibet Plateau based on Google data;additionally,comparative experiments showed that the MSCANet proposed had obvious improvement in lake detection accuracy and morphological integrity.Finally,we transferred the pre-trained optimal model to the Landsat-8 and Sentinel-2A dataset to verify the generalization of the MSCANet.展开更多
基金Supported by the Project of Guangzhou Science and Technology (202102020591,202007010004,202007040005)。
文摘Background Owing to the rapid development of deep networks, single-image deraining tasks have progressed significantly. Various architectures have been designed to recursively or directly remove rain, and most rain streaks can be removed using existing deraining methods. However, many of them cause detail loss, resulting in visual artifacts. Method To resolve this issue, we propose a novel unrolling rain-guided detail recovery network(URDRN) for single-image deraining based on the observation that the most degraded areas of a background image tend to be the most rain-corrupted regions. Furthermore, to address the problem that most existing deep-learningbased methods trivialize the observation model and simply learn end-to-end mapping, the proposed URDRN unrolls a single-image deraining task into two subproblems: rain extraction and detail recovery. Result Specifically, first, a context aggregation attention network is introduced to effectively extract rain streaks;thereafter, a rain attention map is generated as an indicator to guide the detail recovery process. For the detail recovery sub-network, with the guidance of the rain attention map, a simple encoder–decoder model is sufficient to recover the lost details.Experiments on several well-known benchmark datasets show that the proposed approach can achieve performance similar to those of other state-of-the-art methods.
基金supported by the Second Tibetan Plateau Scientific Expedition and Research(STEP)program under Grant 2019QZKK0106the Science and Technology Major Project of Henan Province under Grant 201400210900.
文摘Qinghai-Tibet Plateau lakes are important carriers of water resources in the‘Asian’s Water Tower’,and it is of great significance to grasp the spatial distribution of plateau lakes for the climate,ecological environment,and regional water cycle.However,the differences in spatial-spectral characteristics of various types of plateau lakes,and the complex background information of plateau both influence the extraction effect of lakes.Therefore,it is a great challenge to completely and effectively extract plateau lakes.In this study,we proposed a multiscale contextual information aggregation network,termed MSCANet,to automatically extract Plateau lake regions.It consists of three main components:a multiscale lake feature encoder,a feature decoder,and a Multicore Pyramid Pooling Module(MPPM).The multiscale lake feature encoder suppressed noise interference to capture multiscale spatial-spectral information from heterogeneous scenes.The MPPM module aggregated the contextual information of various lakes globally.We applied the MSCANet to the lake extraction of the Qinghai-Tibet Plateau based on Google data;additionally,comparative experiments showed that the MSCANet proposed had obvious improvement in lake detection accuracy and morphological integrity.Finally,we transferred the pre-trained optimal model to the Landsat-8 and Sentinel-2A dataset to verify the generalization of the MSCANet.
