Fast and efficient contrast‐enhanced super‐resolution without real‐world data using concatenated recursive compressor–decompressor network

The authors propose a novel model called concatenated recursive compressor–decompressor network (CRCDNet) for contrast‐enhanced super‐resolution. The characteristics of authors’ model can be summarised as follows. First, a compression–decompression process reduces the computational complexity compared with the general fully convolutional model. Second, an internal/external skip‐connection is used to preserve information of the preceding layers. Finally, by employing a recursive module, authors’ model has a small number of parameters, yet is a deep and robust network. The authors apply authors’ proposed network to license plate images. As a real application, license plates can provide important evidence for investigation of crimes and for security, but it is very difficult to collect the vast amounts of license plates required for analysis based on a data‐driven approach. To solve this problem, the authors generated virtual datasets to train authors’ model, while analysing the performance with real license plate datasets. Authors’ method achieves better performance than the state‐of‐the‐art models on license plate images.