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Found 10 papers, 5 papers with code
Learn From Orientation Prior for Radiograph Super-Resolution: Orientation Operator Transformer
Conclusions: In this study, we propose a novel framework called $O^{2}$former for radiological image super-resolution tasks, which improves the reconstruction model's performance by introducing an orientation operator and multi-scale feature fusion strategy.
Target-oriented Domain Adaptation for Infrared Image Super-Resolution
DASRGAN operates on the synergy of two key components: 1) Texture-Oriented Adaptation (TOA) to refine texture details meticulously, and 2) Noise-Oriented Adaptation (NOA), dedicated to minimizing noise transfer.
Deep Image Compression Using Scene Text Quality Assessment
We developed a scene text image quality assessment model to assess text quality in compressed images.
Infrared Image Super-Resolution: Systematic Review, and Future Trends
Image Super-Resolution (SR) is essential for a wide range of computer vision and image processing tasks.
A Scene-Text Synthesis Engine Achieved Through Learning from Decomposed Real-World Data
Some of these studies have proposed generating scene-text images through learning; however, owing to the absence of a suitable training dataset, unsupervised frameworks have been explored to learn from existing real-world data, which might not yield reliable performance.
Stroke-Based Scene Text Erasing Using Synthetic Data for Training
To compensate for the lack of pairwise real-world data, we made considerable use of synthetic text after additional enhancement and subsequently trained our model only on the dataset generated by the improved synthetic text engine.
Fidelity-Controllable Extreme Image Compression with Generative Adversarial Networks
To address both of the drawbacks, our method adopts two-stage training and network interpolation.
Structural Data Recognition with Graph Model Boosting
This paper presents a novel method for structural data recognition using a large number of graph models.
Automatic Generation of Typographic Font from a Small Font Subset
The proposed method uses strokes from given samples for font generation.
