Patch Grid-Based Quality Assessment for Aerial Visible-to-Infrared Image Translation

Aerial visible-to-infrared image translation expands infrared datasets by generating infrared images from visible images for various applications in aerial remote sensing. However, existing image translation methods do not consistently produce high-quality results for each image. In practice, it is crucial to evaluate and filter for high-quality generated images to ensure they are suitable for subsequent tasks. Additionally, generated images often lack paired data for effective evaluation. In this letter, we propose patch grid-based quality assessment (PGQA) for assessing the quality of translated infrared images from visible images in aerial remote sensing. This method decomposes global authenticity and similarity into local authenticity and similarity because the lack of paired data makes it difficult to assess the authenticity of a picture. Specifically, the method first grids real infrared images and trains them with convolutional autoencoder (CAE) networks. Similar components and contents naturally share similar feature representations, so the generated infrared images are fed into the trained autoencoder model by the same grid operation. If the average reconstruction loss is lower, the image quality is higher. This approach enables effective quality assessment of generated infrared images and is consistent with other paired methods. It enhances the utility of generated images and increases their practical value in deep learning applications. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/fan-lmw/PGQA.git</uri>.