用现成超分模型恢复图像高比特色深，细节更清晰。
Bit-depth color recovery via off-the-shelf super-resolution models
- 利用超分模型提取图像先验信息，辅助色深恢复
- 在基准数据集上优于现有方法，实现像素级色彩细节还原
- 适合图像编辑、视频处理等需要高精度色彩的应用
成像技术进步使硬件支持每通道10至16位比特，便于图像编辑与视频处理中的精确操作。尽管深度神经网络有望恢复高比特深度表示，但现有方法常依赖尺度不变图像信息，在特定场景下性能受限。本文提出一种新方法，通过超分辨率架构从图像中提取详细先验信息。利用超分过程中生成的插值数据，实现像素级精细色彩细节恢复。此外，实验表明，超分过程学习的空间特征对细节色深信息恢复具有显著贡献。在基准数据集上的实验表明，该方法优于当前最优方法，凸显超分辨率在高保真色彩还原中的潜力。
原文摘要 · Abstract (English)
Advancements in imaging technology have enabled hardware to support 10 to 16 bits per channel, facilitating precise manipulation in applications like image editing and video processing. While deep neural networks promise to recover high bit-depth representations, existing methods often rely on scale-invariant image information, limiting performance in certain scenarios. In this paper, we introduce a novel approach that integrates a super-resolution architecture to extract detailed a priori information from images. By leveraging interpolated data generated during the super-resolution process, our method achieves pixel-level recovery of fine-grained color details. Additionally, we demonstrate that spatial features learned through the super-resolution process significantly contribute to the recovery of detailed color depth information. Experiments on benchmark datasets demonstrate that our approach outperforms state-of-the-art methods, highlighting the potential of super-resolution for high-fidelity color restoration.
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