标题： 通过现成的超分辨率模型进行位深度颜色恢复 显示英文标题

标题： Bit-depth color recovery via off-the-shelf super-resolution models

摘要： 成像技术的进步使硬件能够支持每通道10到16位，从而在图像编辑和视频处理等应用中实现了精确操作。 虽然深度神经网络有望恢复高比特深度表示，但现有方法通常依赖于尺度不变的图像信息，在某些场景下限制了性能。 在本文中，我们引入了一种新方法，该方法结合了超分辨率架构，以从图像中提取详细的先验信息。 通过利用超分辨率过程中生成的插值数据，我们的方法实现了像素级的细粒度颜色细节恢复。 此外，我们证明了通过超分辨率过程学习的空间特征对详细颜色深度信息的恢复有显著贡献。 在基准数据集上的实验表明，我们的方法优于最先进的方法，突显了超分辨率在高保真颜色恢复中的潜力。 显示英文摘要

摘要： 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.