  HDR(High Dynamic Range) video can reproduce realistic scenes more
realistically, with a wider gamut and broader brightness range. HDR video
resources are still scarce, and most videos are still stored in SDR (Standard
Dynamic Range) format. Therefore, SDRTV-to-HDRTV Conversion (SDR video to HDR
video) can significantly enhance the user's video viewing experience. Since the
correlation between adjacent video frames is very high, the method utilizing
the information of multiple frames can improve the quality of the converted
HDRTV. Therefore, we propose a multi-frame fusion neural network
\textbf{DSLNet} for SDRTV to HDRTV conversion. We first propose a dynamic
spatial-temporal feature alignment module \textbf{DMFA}, which can align and
fuse multi-frame. Then a novel spatial-temporal feature modulation module
\textbf{STFM}, STFM extracts spatial-temporal information of adjacent frames
for more accurate feature modulation. Finally, we design a quality enhancement
module \textbf{LKQE} with large kernels, which can enhance the quality of
generated HDR videos. To evaluate the performance of the proposed method, we
construct a corresponding multi-frame dataset using HDR video of the HDR10
standard to conduct a comprehensive evaluation of different methods. The
experimental results show that our method obtains state-of-the-art performance.
The dataset and code will be released.
