CVMMNov 27, 2020

Rethinking deinterlacing for early interlaced videos

arXiv:2011.13675v21 citations
AI Analysis

This work is significant for researchers and archivists working on the high-definition reconstruction of early videos, as it tackles a previously unaddressed problem of complex interlacing artifacts.

This paper addresses the problem of interlacing artifacts in early videos, which existing deinterlacing methods struggle with due to their focus on simpler scanning systems. The authors propose a two-stage deinterlacing network (DIN) that effectively removes complex artifacts from these videos.

With the rapid development of image restoration techniques, high-definition reconstruction of early videos has achieved impressive results. However, there are few studies about the interlacing artifacts that often appear in early videos and significantly affect visual perception. Traditional deinterlacing approaches are mainly focused on early interlacing scanning systems and thus cannot handle the complex and complicated artifacts in real-world early interlaced videos. Hence, this paper proposes a specific deinterlacing network (DIN), which is motivated by the traditional deinterlacing strategy. The proposed DIN consists of two stages, i.e., a cooperative vertical interpolation stage for split fields, and a merging stage that is applied to perceive movements and remove ghost artifacts. Experimental results demonstrate that the proposed method can effectively remove complex artifacts in early interlaced videos.

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