A comparative study of image restoration networks for general backbone network design

Z You, J Gu, Z Li, X Cai, K Zhu, T Xue… - arXiv preprint arXiv …, 2024 - arxiv.org

With the rapid advancement of Vision Language Models (VLMs), VLM-based Image Quality
Assessment (IQA) seeks to describe image quality linguistically to align with human …

被引用次数：1 相关文章所有 2 个版本

[PDF] arxiv.org

Learning A Low-Level Vision Generalist via Visual Task Prompt

X Chen, Y Liu, Y Pu, W Zhang, J Zhou, Y Qiao… - arXiv preprint arXiv …, 2024 - arxiv.org

Building a unified model for general low-level vision tasks holds significant research and
practical value. Current methods encounter several critical issues. Multi-task restoration …

Interpreting Low-level Vision Models with Causal Effect Maps

J Hu, J Gu, S Yu, F Yu, Z Li, Z You, C Lu… - arXiv preprint arXiv …, 2024 - arxiv.org

Deep neural networks have significantly improved the performance of low-level vision tasks
but also increased the difficulty of interpretability. A deep understanding of deep models is …

Bracketing Image Restoration and Enhancement with High-Low Frequency Decomposition

G Chen, K Dai, K Yang, T Hu, X Chen, Y Yang… - arXiv preprint arXiv …, 2024 - arxiv.org

In real-world scenarios, due to a series of image degradations, obtaining high-quality, clear
content photos is challenging. While significant progress has been made in synthesizing …

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[PDF] arxiv.org

Learning to See Low-Light Images via Feature Domain Adaptation

Q Yang, H Yue, L Zhang, Y Liu, J Yang - arXiv preprint arXiv …, 2023 - arxiv.org

Raw low light image enhancement (LLIE) has achieved much better performance than the
sRGB domain enhancement methods due to the merits of raw data. However, the ambiguity …

被引用次数：1 相关文章所有 2 个版本