Bit-depth is the number of bits for each color channel of a pixel in an
image. Although many modern displays support unprecedented higher bit-depth to
show more realistic and natural colors with a high dynamic range, most media
sources are still in bit-depth of 8 or lower. Since insufficient bit-depth may
generate annoying false contours or lose detailed visual appearance, bit-depth
expansion (BDE) from low bit-depth (LBD) images to high bit-depth (HBD) images
becomes more and more important. In this paper, we adopt a learning-based
approach for BDE and propose a novel CNN-based bit-depth expansion network
(BitNet) that can effectively remove false contours and restore visual details
at the same time. We have carefully designed our BitNet based on an
encoder-decoder architecture with dilated convolutions and a novel multi-scale
feature integration. We have performed various experiments with four different
datasets including MIT-Adobe FiveK, Kodak, ESPL v2, and TESTIMAGES, and our
proposed BitNet has achieved state-of-the-art performance in terms of PSNR and
SSIM among other existing BDE methods and famous CNN-based image processing
networks. Unlike previous methods that separately process each color channel,
we treat all RGB channels at once and have greatly improved color restoration.
In addition, our network has shown the fastest computational speed in near
real-time.

该研究提出了一种基于 CNN 的比特深度扩展网络 (BitNet)，其采用编码器 - 解码器架构和多尺度特征集成，可将低比特深度图像转换成高比特深度图像；相较于之前的方法，BitNet 能够同时处理所有 RGB 通道，显著提高了颜色的恢复效果，在四个数据集上表现优于其他现有 BDE 方法和 CNN 图像处理网络，并具有最快的近实时计算速度。