  Bit-depth expansion (BDE) is one of the emerging technologies to display high
bit-depth (HBD) image from low bit-depth (LBD) source. Existing BDE methods
have no unified solution for various BDE situations, and directly learn a
mapping for each pixel from LBD image to the desired value in HBD image, which
may change the given high-order bits and lead to a huge deviation from the
ground truth. In this paper, we design a bit restoration network (BRNet) to
learn a weight for each pixel, which indicates the ratio of the replenished
value within a rational range, invoking an accurate solution without modifying
the given high-order bit information. To make the network adaptive for any
bit-depth degradation, we investigate the issue in an optimization perspective
and train the network under progressive training strategy for better
performance. Moreover, we employ Wasserstein distance as a visual quality
indicator to evaluate the difference of color distribution between restored
image and the ground truth. Experimental results show our method can restore
colorful images with fewer artifacts and false contours, and outperforms
state-of-the-art methods with higher PSNR/SSIM results and lower Wasserstein
distance. The source code will be made available at
https://github.com/yuqing-liu-dut/bit-depth-expansion
