用于真实世界SDRTV到HDRTV转换的双重逆退化网络
Dual Inverse Degradation Network for Real-World SDRTV-to-HDRTV Conversion
AI总结：
本文提出DIDNet网络，将SDRTV到HDRTV转换建模为双重退化恢复任务，通过中间辅助损失、时空特征对齐、双调制卷积和小波注意力模块，在抑制编码伪影的同时实现高质量逆色调映射。
摘要：
在本研究中，我们针对原生HDRTV内容数量有限这一现状，探讨将标准动态范围电视（SDRTV）内容转换为高动态范围电视（HDRTV）的新兴需求。该转换中的一个主要技术挑战是SDRTV中固有的编码伪影会被加剧，从而损害最终HDRTV的质量。为解决这一问题，我们的方法提出了一种新思路，将SDRTV到HDRTV的转换概念化为涉及双重退化恢复的复合任务，包括逆色调映射与视频恢复。我们提出了双重逆降级SDRTV到HDRTV网络（DIDNet），该网络能够准确执行逆色调映射，同时防止编码伪影被放大，从而显著提升视觉质量。DIDNet集成了一个中间辅助损失函数，以有效分离双重退化恢复任务，并在端到端训练过程中高效学习伪影减少和逆色调映射。此外，DIDNet引入了一个时空特征对齐模块用于视频帧融合，从而增强纹理质量并减少伪影。该架构还包括一种双调制卷积机制，用于优化逆色调映射。考虑到与SDRTV相比，HDRTV具有更丰富的纹理和高频信息，我们进一步引入了一个小波注意力模块来增强频率特征。我们的方法在定量性能和视觉质量方面均显著优于现有的最先进技术。
英文摘要：
In this study, we address the emerging necessity of converting Standard Dynamic Range Television (SDRTV) content into High Dynamic Range Television (HDRTV) in light of the limited number of native HDRTV content. A principal technical challenge in this conversion is the exacerbation of coding artifacts inherent in SDRTV, which detrimentally impacts the quality of the resulting HDRTV. To address this issue, our method introduces a novel approach that conceptualizes the SDRTV-to-HDRTV conversion as a composite task involving dual degradation restoration. This encompasses inverse tone mapping in conjunction with video restoration. We propose Dual Inversion Downgraded SDRTV to HDRTV Network (DIDNet), which can accurately perform inverse tone mapping while preventing encoding artifacts from being amplified, thereby significantly improving visual quality. DIDNet integrates an intermediate auxiliary loss function to effectively separate the dual degradation restoration tasks and efficient learning of both artifact reduction and inverse tone mapping during end-to-end training. Additionally, DIDNet introduces a spatio-temporal feature alignment module for video frame fusion, which augments texture quality and reduces artifacts. The architecture further includes a dual-modulation convolution mechanism for optimized inverse tone mapping. Recognizing the richer texture and high-frequency information in HDRTV compared to SDRTV, we further introduce a wavelet attention module to enhance frequency features. Our approach demonstrates marked superiority over existing state-of-the-art techniques in terms of quantitative performance and visual quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
