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

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

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KEYWORDS

Semantics

Data modeling

Education and training

Image enhancement

Super resolution

Image segmentation

Buildings

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