Low-Light Robust Face Image Super-Resolution via Neuro-Fuzzy Inferencing-Based Locality Constrained Representation

Face super-resolution (FSR) has recently become an interesting topic of study in the fields of image processing and computer vision. However, current FSR methods are still incapable of dealing with those faces suffering from low-light issues, which are frequently caused by uneven lighting conditions in the environment. Therefore, a novel neuro-fuzzy inferencing-based locality-constrained representation (NFILcR) method for low-light robust FSR is proposed in this article. In NFILcR, the adaptive neuro-fuzzy inferencing system (ANFIS) and locality-constrained representation approach are integrated into one unified framework to super-resolve the captured low-light and low-resolution (LR) faces. Specifically, an adaptive neuro-fuzzy inferencing-based low-light factor estimation (ANFILLE) model is introduced that estimates the low-light factor in the test images. Furthermore, the estimated low-light factor is incorporated into the objective function of the proposed NFILcR method. These contributions make the proposed method robust against the low-light issue. The experimental results on benchmark face datasets and real-world images show that the proposed NFILcR method outperforms the other FSR methods that have been compared.