Single-Image Super-Resolution Analysis in  DCT Spectral Domain

Aydin, Onur; Cinbiş, Ramazan Gökberk

doi:10.17694/bajece.714293

Cited by 3 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It also emphasizes the error of high-frequency components by proposing a new weighted Euclidean loss. Aydin et al [23] predict DCT coefficients that can reconstruct a high-resolution image through a fully connected (FC) layer in the DCT spectral domain after extending the input image to a target magnification through interpolation. The loss is defined as the mean square error with the DCT coefficient for the corresponding high-resolution image, which showed the possibility of CNN learning in the DCT spectral domain.…”

Section: Frequency Domain Super-resolutionmentioning

confidence: 99%

Single Image Super-Resolution with Arbitrary Magnification Based on High-Frequency Attention Network

Yun

Yoo

2022

Mathematics

View full text Add to dashboard Cite

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.

show abstract

Section: Frequency Domain Super-resolutionmentioning

confidence: 99%