LAUSR

Single image super-resolution (SR), as a basic computer vision task, has been widely studied. However, existing single image SR methods, applied in images with normal light, perform poorly on low-light images. To address this limitation, a method of structure-preserving and color-restoring up-sampling for single low-light image is proposed. Theoretical and experimental analysis indicates that existing methods suffer negative effects due to the suppressed color and the weakened texture in low-light image. Therefore, combined with the Retinex theory, the single low-light image up-sampling model is established for the first time, which avoids the conflict between color and texture by distinguishing reflectance and illumination. Further, we develop a structure-preserving and color-restoring up-sampling network for single low-light image SR. In the network, the reflectance and illumination components are obtained by decomposing the observed image, and then up-sampling of reflectance and enhancement of illumination are performed to complete the primary SR. In addition, the gradient information is reconstructed and fused into the up-sampling process to further enrich the SR texture. Experiments demonstrate that our method obtains competitive qualitative and quantitative evaluation on the produced dataset and real-world images.