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English(EN) SRGAN-CKAN: Expressive Super-Resolution with Nonlinear Functional Operators under Minimal Resources

SRGAN-CKAN 通过高效的局部算子改进图像超分辨率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 SRGAN-CKAN，一个新颖的单图像超分辨率框架，通过改进局部算子来增强细节重建。该方法将卷积 Kolmogorov-Arnold 网络 (CKAN) 集成到对抗学习设置中，将卷积重新表述为非线性块变换。该方法使用基于样条的函数表示来有效地建模复杂结构和高频纹理，在极少的计算资源下实现了感知质量和重建保真度之间的平衡。 AI

影响 引入了一种更高效的图像超分辨率方法，有可能在性能较低的硬件上实现更高质量的重建。

排序理由 这是一篇详细介绍图像超分辨率新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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SRGAN-CKAN 通过高效的局部算子改进图像超分辨率

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这是一篇详细介绍图像超分辨率新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Roberto Isai Navaro-Avi\~na, Eduardo Said Merin-Martinez, Andres Mendez-Vazquez, Eduardo Rodriguez-Tello ·

    SRGAN-CKAN：极简资源下的非线性函数算子实现富有表现力的超分辨率

    arXiv:2605.01459v1 Announce Type: new Abstract: Single-Image Super-Resolution (SISR) aims to reconstruct a High-Resolution (HR) image from a Low-Resolution (LR) observation, a fundamentally ill-posed problem where high-frequency details are severely degraded at large upscaling fa…

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