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Image super-resolution (SR) is a classic problem in image restoration, which aims to reconstruct a high-resolution (HR) image with realistic details from a low-resolution (LR) image. The challenge lies in the fact that SR models need to ‘imagine’ details that may not exist in the LR image. Although recent state-of-art super-resolution models have made great improvements in performance, the models we have found are trained for open domain SR use. Our research addresses the lack of high-quality architectural images on the internet, including popular web mapping platforms like Google Maps 360◦ Streetview and Bing Streetside by proposing a super-resolution model refined for architectural images. We use a domain-specific dataset of architectural images of building facades collected by our team to train our model in order to analyze the performance of a specialized model over general super-resolution models. We modify the model architecture to generate better results in terms of perceptual realism, sharpness and fine details. In this paper, we introduce a super-resolution model specialized in architectural images to fill a void in the state-of-art SR model offerings.