Diffusion-Based Bit-Depth Expansion

作者：Riyu Lu, Lingyu Zhu, Baoliang Chen, Xiaopeng Fan, Shiqi Wang · 年份：2024 · DOI：10.1109/mmsp61759.2024.10743597 · 被引用次数：5 · 研究领域：Advancements in Photolithography Techniques

Diffusion-based generative models have achieved remarkable success across a variety of applications. However, the potential application for bit-depth expansion has not been extensively studied. This paper introduces a wavelet-based diffusion model for the bit-depth expansion task. In this method, the image is first decomposed into low and high-frequency components via wavelet transformation. This decomposition allows for targeted processing by specialized modules and reduces computational complexity by lowering the image resolution. The low-frequency component is processed in both the forward diffusion and reverse denoising stages. Meanwhile, the high-frequency components are filtered by the High Frequency Denoising Filter (HFDF) to eliminate noise and artifacts. Finally, the low and high-frequency components are recombined into a predicted high-bit-depth image through inverse wavelet transformation. Experimental results demonstrate the superiority of the proposed method in producing perceptually compelling outputs that outperform previous methods.