CSBNet: Leveraging Edge Intelligence for Multigranularity Low-Light Image Enhancement

Yong Wang, Lijun Jiang, Zilong Du, Bo Li, Wenming Yang

Abstract: Low-light (LOL) conditions constantly restrict the performance of Internet of Things (IoT) image sensors, thereby impacting image quality and the precision of visual data analysis. The emerging edge intelligence is crucial for LOL image enhancement in improving image quality and data support reliability for IoT systems, which in turn fosters the intelligence and automation progress of the IoT. The enhancement of LOL images necessitates the restoration of both contextual information and spatial details, maintaining the semantic content of the original image and the point-to-point correspondence between inputs and outputs. However, existing methods predominantly concentrate on one aspect, either contextual information or spatial details, making it difficult to simultaneously balance both. To overcome this challenge, we introduce a novel two-branch network, the context-space balance network (CSBNet), and tailored for LOL image enhancement. It comprises a contextual information recovery network (CIRNet), which adeptly extracts contextual information from multiscale LOL images, and a spatial information recovery network (SIRNet), which is designed to preserve spatial details at the original resolution. We also implement a context-space feature fusion (CSFF) module to seamlessly integrate contextual information with spatial details. Qualitative and quantitative experimental results demonstrate that our CSBNet can better handle various kinds of degradations in lowlight images compared with state-of-the-art solutions on the benchmark LOL dataset. The source code of CSBNet is available at https://github.com/Loong161/CSBNet.

External IDs:dblp:journals/iotj/WangJDLY25