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Found 6 papers, 5 papers with code
Toward Generalized Image Quality Assessment: Relaxing the Perfect Reference Quality Assumption
To relax the assumption of perfect reference image quality, we build a large-scale IQA database, namely DiffIQA, containing approximately 180, 000 images generated by a diffusion-based image enhancer with adjustable hyper-parameters.
Full reference image quality assessment
Full-Reference Image Quality Assessment
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Generalized and Efficient 2D Gaussian Splatting for Arbitrary-scale Super-Resolution
Implicit Neural Representation (INR) has been successfully employed for Arbitrary-scale Super-Resolution (ASR).
SSL: A Self-similarity Loss for Improving Generative Image Super-resolution
Based on the fact that natural images exhibit high self-similarities, i. e., a local patch can have many similar patches to it in the whole image, in this work we propose a simple yet effective self-similarity loss (SSL) to improve the performance of generative Real-ISR models, enhancing the hallucination of structural and textural details while reducing the unpleasant visual artifacts.
Orion-14B: Open-source Multilingual Large Language Models
In this study, we introduce Orion-14B, a collection of multilingual large language models with 14 billion parameters.
Human Guided Ground-truth Generation for Realistic Image Super-resolution
A human guided GT image dataset with both positive and negative samples is then constructed, and a loss function is proposed to train the Real-ISR models.
Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features
Firstly, we proposed a novel Orientation-Aware feature extraction and fusion Module (OAM), which contains a mixture of 1D and 2D convolutional kernels (i. e., 5 x 1, 1 x 5, and 3 x 3) for extracting orientation-aware features.
