其他基准方法: DCLS:一种先进的核预测(KP)方法,作为参考。 Related work “Blind Image Super-Resolution with Spatially Variant Degradations”; “Blind Super-Resolution Kernel Estimation using an Internal-GAN”; “Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels”。 ...
Recovering degraded low-resolution text images is challenging, especially for Chinese text images with complex strokes and severe degradation in real-world scenarios. Ensuring both text fidelity and style realness is crucial for high-quality text image super-resolution. Recently, diffusion models have ...
在推理速度和内存使用方面,它比相关先前技术明显更好。 2、Diffusion-based Blind Text Image Super-Resolution 恢复退化的低分辨率文本图像是一项具有挑战性的任务,特别是在现实复杂情况下处理带有复杂笔画和严重退化的中文文本图像。保证文本的保真度和真实性风格对于高质量的文本图像超分辨率非常重要。最近,扩散模型在自然...
在推理速度和内存使用方面,它比相关先前技术明显更好。 2、Diffusion-based Blind Text Image Super-Resolution 恢复退化的低分辨率文本图像是一项具有挑战性的任务,特别是在现实复杂情况下处理带有复杂笔画和严重退化的中文文本图像。保证文本的保真度和真实性风格对于高质量的文本图像超分辨率非常重要。最近,扩散模型在自然...
CDFormer: When Degradation Prediction Embraces Diffusion Model for Blind Image Super-Resolution 文章解读:http://www.studyai.com/xueshu/paper/detail/eeb6c71993 文章链接:(https://openaccess.thecvf.com/content/CVPR2024/html/Liu_CDFormer_When_Degradation_Prediction_Embraces_Diffusion_Model_for_Blind_Ima...
2、Diffusion-based Blind Text Image Super-Resolution 恢复退化的低分辨率文本图像是一项具有挑战性的任务,特别是在现实复杂情况下处理带有复杂笔画和严重退化的中文文本图像。保证文本的保真度和真实性风格对于高质量的文本图像超分辨率非常重要。最近,扩散模型在自然图像合成和恢复方面取得成功,因为它们具有强大的数据分布...
《Diffusion-based Blind Text Image Super-Resolution》(CVPR 2024) GitHub: github.com/YuzheZhang-1999/DiffTSR [fig3]《Parameter Efficient Self-supervised Geospatial Domain Adaptation》(CVPR 2024) GitHub: github.com/HSG-AIML/GDA [fig7]《GeoLRM: Geometry-Aware Large Reconstruction Model for High-...
We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution (SR). Specifically, by employing our time-aware encoder, we can achieve promising restoration results without altering the pre-trained synthesis model, thereby...
We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution. Specifically, by employing our time-aware encoder, we can achieve promising restoration results without altering the pre-trained synthesis model, thereby ...
Despite this success, they have not outperformed state-of-the-art GAN models on the more challenging blind super-resolution task, where the input images are out of distribution, with unknown degradations. This paper introduces SR3+, a diffusion-based model for blind super-resolution, establishing...