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Due to hardware constraints, standard off-the-shelf digital cameras suffersfrom low dynamic range (LDR) and low frame per second (FPS) outputs. Previousworks in high dynamic range (HDR) video reconstruction uses sequence ofalternating exposure LDR frames as input, and align the neighbouring framesusing optical flow based networks. However, these methods often result inmotion artifacts in challenging situations. This is because, the alternateexposure frames have to be exposure matched in order to apply alignment usingoptical flow. Hence, over-saturation and noise in the LDR frames results ininaccurate alignment. To this end, we propose to align the input LDR framesusing a pre-trained video frame interpolation network. This results in betteralignment of LDR frames, since we circumvent the error-prone exposure matchingstep, and directly generate intermediate missing frames from the same exposureinputs. Furthermore, it allows us to generate high FPS HDR videos byrecursively interpolating the intermediate frames. Through this work, wepropose to use video frame interpolation for HDR video reconstruction, andpresent the first method to generate high FPS HDR videos. Experimental resultsdemonstrate the efficacy of the proposed framework against optical flow basedalignment methods, with an absolute improvement of 2.4 PSNR value on standardHDR video datasets [1], [2] and further benchmark our method for high FPS HDRvideo generation.

DeepHS-HDRVideo: Deep High Speed High Dynamic Range Video Reconstruction