更多：逐位位深度恢复
A Little Bit More: Bitplane-Wise Bit-Depth Recovery
摘要：
成像传感器以10- 12位的动态范围（即，1024- 4096色调值）。然后在相机上处理传感器图像并最终量化为仅8位（即，256个音调值）以符合流行的编码标准。有许多重要的应用，如高位深显示和照片编辑，其中恢复丢失的位深是有益的。深度神经网络在这种位深度重建任务中是有效的。给定量化的低位深度图像作为输入，现有的深度学习方法采用单次方法，该方法尝试（1）直接估计高位深度图像，或者（2）直接估计高位深度图像和低位深度图像之间的残差。相比之下，我们提出了一个训练和推理策略，恢复残差图像逐位平面。我们的位平面学习框架具有允许在训练期间进行多级监督的优势，并且能够使用简单的网络架构获得最先进的结果。我们测试了我们提出的方法广泛的几个图像数据集，并证明了从0.5dB到2.3dB的PSNR比以前的方法取决于量化水平的改善。
英文摘要：
Imaging sensors digitize incoming scene light at a dynamic range of 10--12 bits (i.e., 1024--4096 tonal values). The sensor image is then processed onboard the camera and finally quantized to only 8 bits (i.e., 256 tonal values) to conform to prevailing encoding standards. There are a number of important applications, such as high-bit-depth displays and photo editing, where it is beneficial to recover the lost bit depth. Deep neural networks are effective at this bit-depth reconstruction task. Given the quantized low-bit-depth image as input, existing deep learning methods employ a single-shot approach that attempts to either (1) directly estimate the high-bit-depth image, or (2) directly estimate the residual between the high- and low-bit-depth images. In contrast, we propose a training and inference strategy that recovers the residual image bitplane-by-bitplane. Our bitplane-wise learning framework has the advantage of allowing for multiple levels of supervision during training and is able to obtain state-of-the-art results using a simple network architecture. We test our proposed method extensively on several image datasets and demonstrate an improvement from 0.5dB to 2.3dB PSNR over prior methods depending on the quantization level.
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