A semantic and attribute-based dataset for image super resolution

Hu Tan; Yu Shi; Lei Li

doi:10.1117/12.3034131

13 June 2024 A semantic and attribute-based dataset for image super resolution

Hu Tan, Yu Shi, Lei Li

Author Affiliations +

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 1318041 (2024) https://doi.org/10.1117/12.3034131
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

Despite the remarkable advancements propelled by deep learning in Image Super Resolution, the enigmatic essence of deep neural networks introduces ongoing challenges, particularly emphasizing the necessity for interpretability in the elucidation of how image features are transmuted into enhanced high-resolution outputs. With the strategic importance of training datasets in view, we introduce a rigorously curated UHD image dataset endowed with distinct attributes. This dataset, systematically categorized and annotated, includes a diverse assortment of semantic textures such as windowpanes, trees, skyscrapers, and architectural elements. Its design is intended to streamline the training and testing of a variety of SISR models, accommodating numerous resolution scales and image formats predicated on high-quality original images. It is developed with the goal of improving the identification of standard mapping functions within SISR, thus facilitating the generation of more productive and versatile models.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hu Tan, Yu Shi, and Lei Li "A semantic and attribute-based dataset for image super resolution", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 1318041 (13 June 2024); https://doi.org/10.1117/12.3034131

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE