With the widespread application of infrared imaging technology in various fields, infrared image super-resolution has become a key technology that can improve image quality and extract more information. However, infrared images typically have lower resolution, limiting their details and texture information, and are more prone to noise during the imaging process, which further reduces image quality. In addition, the acquisition cost of infrared images is relatively high, resulting in fewer scenes in infrared images. These factors greatly limit the performance and generalization ability of infrared super-resolution algorithms.

In order to promote the research and development of infrared image super-resolution technology and solve these problems, we have constructed a multi scene infrared super-resolution image database. This database covers multiple scenarios and conditions to reflect a wide range of practical applications. The resolution of the image is mainly 1280 * 1024, 1920 * 1080, and 640 * 512. High-resolution and high-quality images Provide rich details and texture information. This database is conducive to utilizing high-quality infrared image data from the real world to develop more robust and accurate super-resolution algorithms, promoting the application of infrared super-resolution technology in various fields.

Our research aims to establish a high-resolution multi scene real-world infrared image super-resolution database, providing high-quality real-world data, enabling researchers to evaluate and compare the performance of different super-resolution algorithms, and develop new accurate algorithms. This database contains a total of 7224 images from nine categories of scenes, including sea view images, animal images, industrial scenes, cityscapes, natural landscapes, indoor images, portraits, surveillance image, and vehicle perspective road images. It is divided into a test set of 726 images and a training set of 6498 images, all of which are single channel images saved in BMP format.

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