In this paper, we introduce the single image glare removal (SIGR) task. SIGR aims to eliminate glare or light caused by external environment in lighting scene. To efficiently improve natural image quality and usability, many research tasks, such as image deraining and shadow removal, have been investigated a lot. However, SIGR is still underexplored. Therefore, we propose to construct a dataset and explore deep learning-based models for SIGR. Our contributions can be summarized as follows: (1) We establish a new benchmark dataset for SIGR, termed De-Glare, aiming to propel research of SIGR. This dataset comprises pairs of {glare, glare-free} images sourced from both real-world and synthetic data, utilized for training and evaluating models. (2) We conduct a comprehensive benchmarking of extensive state-of-the-art (SoTA) methods on the constructed De-Glare dataset and provide insightful analyses based on the results. (3) An innovative approach for single image glare removal employing a multi-scale generative adversarial network (GR-GAN) model is proposed. Glare images typically exhibit irregular glare shapes and cluttered backgrounds. To address these irregular glare patterns, we introduce a deformable convolution-based glare attention detector (GAD) designed to generate an attention map which specifics glare spots or rays in the input image. In pursuit of enhancing the perceptual quality of output image, GR-GAN adaptively filters out irrelevant noises and enhances salient features through a generator with cascaded pyramid neck (CPN) network. This work can provide useful insights for developing better SIGR models. Without specific tuning, our method achieves the SoTA results on multiple computer vision tasks, including the image deraining and image shadow removal.