  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.
