Deep Wavelet Frequency Laplacian Pyramid Network for Vehicle Logo Super-Resolution

In intelligent transportation systems, generated high-resolution images promote high-level version tasks compared to captured low-resolution vehicle logo images, which lack adequate details. However, the resolution of vehicle logo images is restricted by factors like hardware. Few attempts have been made with the Laplacian pyramid structure, while images can be represented on multi-scale in the wavelet domain. This paper proposes a progressive reconstruction network with a Laplacian pyramid (WLapsrn) structure to predict the sub-bands of wavelet transformed images. Firstly, we adopted a suitable network architecture for feature extraction using residual blocks for the advanced reconstruction network in the wavelet domain. Then, the Texture-Charbonnier loss function is used to achieve highfrequency sub-bands reconstruction. Ultimately, the inverse 2D discrete stationary wavelet transform is applied to generate predicting HR images from mapped coefficients. Extensive objective and subjective evaluations using benchmark datasets demonstrate that the proposed algorithm achieves superior visual effects compared with the state-of-the-art Laplacian-based algorithms.