Abstract
Among various developments in the field of computer vision, single image super-resolution of images is one of the most essential tasks. However, compared to the integer magnification model for super-resolution, research on arbitrary magnification has been overlooked. In addition, the importance of single image super-resolution at arbitrary magnification is emphasized for tasks such as object recognition and satellite image magnification. In this study, we propose a model that performs arbitrary magnification while retaining the advantages of integer magnification. The proposed model extends the integer magnification image to the target magnification in the discrete cosine transform (DCT) spectral domain. The broadening of the DCT spectral domain results in a lack of high-frequency components. To solve this problem, we propose a high-frequency attention network for arbitrary magnification so that high-frequency information can be restored. In addition, only high-frequency components are extracted from the image with a mask generated by a hyperparameter in the DCT domain. Therefore, the high-frequency components that have a substantial impact on image quality are recovered by this procedure. The proposed framework achieves the performance of an integer magnification and correctly retrieves the high-frequency components lost between the arbitrary magnifications. We experimentally validated our model’s superiority over state-of-the-art models.
Highlights
Owing to convolutional neural networks (CNNs), image super-resolution shows excellent high-resolution reconstruction from low-resolution images
Real-world images taken using CCTV cameras, black boxes, drones, etc., have small object areas, and when the image is resized by general interpolation, it causes blur and lowers the performance of object recognition
In traditional super-resolution learning, each patch unit is obtained from a low-resolution image, i.e., an input image, and a high-resolution image, i.e., a target image, and it is learned through comparison
Summary
Owing to convolutional neural networks (CNNs), image super-resolution shows excellent high-resolution reconstruction from low-resolution images. Real-world images taken using CCTV cameras, black boxes, drones, etc., have small object areas, and when the image is resized by general interpolation, it causes blur and lowers the performance of object recognition. To solve this problem, Lee et al [1] applied the superresolution approach to an image that has a small object area. Lee et al [1] applied the superresolution approach to an image that has a small object area By applying this approach, object recognition accuracy was improved compared to the existing interpolation method
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