Recently, the transformation of standard dynamic range TV (SDRTV) to high dynamic range TV (HDRTV) is in high demand due to the scarcity of HDRTV content. However, the conversion of SDRTV to HDRTV often amplifies the existing coding artifacts in SDRTV which deteriorate the visual quality of the output. In this study, we propose a dual inverse degradation SDRTV-to-HDRTV network DIDNet to address the issue of coding artifact restoration in converted HDRTV, which has not been previously studied. Specifically, we propose a temporal-spatial feature alignment module and dual modulation convolution to remove coding artifacts and enhance color restoration ability. Furthermore, a wavelet attention module is proposed to improve SDRTV features in the frequency domain. An auxiliary loss is introduced to decouple the learning process for effectively restoring from dual degradation. The proposed method outperforms the current state-of-the-art method in terms of quantitative results, visual quality, and inference times, thus enhancing the performance of the SDRTV-to-HDRTV method in real-world scenarios.

翻译：近期，由于高动态范围电视（HDRTV）内容的稀缺，标准动态范围电视（SDRTV）向HDRTV的转换需求日益迫切。然而，SDRTV到HDRTV的转换过程往往会放大SDRTV中存在的编码伪影，从而降低输出视频的视觉质量。本研究提出了一种双重逆退化SDRTV到HDRTV网络DIDNet，以解决转换后HDRTV中编码伪影修复这一此前未被探索的问题。具体而言，我们设计了时空特征对齐模块与双重调制卷积，用于消除编码伪影并增强色彩恢复能力；进一步提出小波注意力模块，从频域角度提升SDRTV特征质量。通过引入辅助损失函数解耦学习过程，实现对双重退化因素的有效修复。实验结果表明，该方法在量化指标、视觉质量与推理速度上均优于现有最优方法，从而增强了真实场景下SDRTV到HDRTV转换的实用性。