Residual-Guided Multiscale Fusion Network for Bit-Depth Enhancement

Bit-depth enhancement (BDE) is a challenging task due to stubborn false contour artifacts and disappeared detailed information. Given the mixture of structural distortions and real edges in low bit-depth (LBD) images, both large and small receptive fields (RFs) are critical for BDE tasks. However, even powerful state-of-the-art CNN-based methods can hardly capture sufficient LBD features under multiple RFs. This paper proposes a residual-guided multiscale fusion network (RMFNet) to explore multiscale features in a residual manner. We find that the shuffling operation provides desired multiscale inputs for effectively distinguishing false contours from real edges without any loss of information. Therefore, we shuffle LBD images to multiple scales and then fully extract residual features under different RFs with corresponding subnets. To facilitate interscale guidance from the global context to the local context, we progressively transfer the encoded residual features between adjacent subnets from top to bottom. We further propose a dual-branch depthwise group fusion (DDGF) module to fully capture inter- and inner correlations of multiscale features with fewer parameters. Finally, extensive experiments show that our algorithm achieves excellent performance improvement both quantitatively and qualitatively, verifying its effectiveness.