Defective pixel concealment is a necessary procedure in infrared image processing and is widely used. However, current approaches are mainly focused on the concealment of isolated pixels and small defective pixel clusters. Consequently, these approaches cannot meet the requirements when applied to infrared detectors with large defective pixel clusters. In this paper, we present a novel and comprehensive approach to processing the image data acquired from infrared imagers with large and small defective pixel clusters. Our approach consists of preprocessing, coarse concealment, high dynamic range enhancement, and fine concealment by generative adversarial networks. Experiments using mid-wave infrared and long-wave infrared images demonstrated that the proposed approach achieves better results than the best conventional approach, known as transforming image completion, with the peak signal-to-noise ratio and structural similarity metrics improved by 2.7063 dB (16.3%) and 0.1951 dB (34.1%), respectively.