Swin enhancer: A low-light smart meter image enhancement method with a multi-layered window attention network

Abstract

Accurate and efficient identification of visual faults in smart meters is significant to the stable operation of the power acquisition system. However, the electricity meter images collected in dim environments have low brightness problems and lack of detail, which will impact subsequent fault identification based on computer vision. From an unsupervised perspective, this paper proposes a low-light smart meter image enhancement method, Swin enhancer. Originating from the Swin Transformer’s window attention mechanism, this paper designs a Multi-layer Swin Transformer Block (MSTB) to extract regional brightness features through the local window’s attention calculation and provide varying degrees of illumination compensation. At the same time, a shift window mechanism is introduced to interact features among the regions and reduce the possibility of overexposure. Extensive experiments on several benchmark and electric meter datasets demonstrate that our method outperforms state-of-the-art methods on multiple image evaluation metrics.