Single image super-resolution, especially SRGAN, can generate photorealistic images from down-sampled images. However, it is difficult to super-resolve originally low resolution images that contain some artifacts and were taken many years ago. In this paper, we focus on the food domain because it is useful for our recipebased web service if we can create better looking super-resolved images without losing content information. Based on the observation that SRGAN learns how to restore realistic high-resolution images from down-sampled ones, we propose two approaches. The first one is a down-sampling method using noise injection to create desirable low-resolution images from high-resolution ones for model training. The second one is to train models for each target domain: we use the beef, bread, chicken and pound cake categories in our experiments. We also propose a novel evaluation method, Xception score. Compared with existing methods using qualitative and quantitative experiments, we find the proposed methods can generate more realistic super-resolved images. CCS CONCEPTS • Computing methodologies → Image processing; Reconstruction; Neural networks;

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In food-related services, image information is as important as text information for users. For example, in recipe search services, users find recipes based not only on text but also images. To promote studies on food images, many datasets have recently been published. However, they have the following three limitations: most of the datasets include only thousands of images, they only take account of images after cooking not during the cooking process, and the images are not linked to any recipes. In this study, we construct the Cookpad Image Dataset, a novel collection of food images taken from Cookpad, the largest recipe search service in the world. The dataset includes more than 1.64 million images after cooking, and it is the largest among existing datasets. Additionally, it includes more than 3.10 million images taken during the cooking process. To the best of our knowledge, there are no datasets that include such images. Furthermore, the dataset is designed to link to an existing recipe corpus and thus, a variety of recipe texts, such as the title, description, ingredients, and process, is available for each image. In this paper, we described our dataset's features in detail and compared it with existing datasets.

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