Efficient implementation of neural network deinterlacing

Guiwon Seo; Hyunsoo Choi; Chulhee Lee

doi:10.1117/12.810571

10 February 2009 Efficient implementation of neural network deinterlacing

Guiwon Seo, Hyunsoo Choi, Chulhee Lee

Proceedings Volume 7245, Image Processing: Algorithms and Systems VII; 724519 (2009) https://doi.org/10.1117/12.810571
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States

Abstract

Interlaced scanning has been widely used in most broadcasting systems. However, there are some undesirable artifacts such as jagged patterns, flickering, and line twitters. Moreover, most recent TV monitors utilize flat panel display technologies such as LCD or PDP monitors and these monitors require progressive formats. Consequently, the conversion of interlaced video into progressive video is required in many applications and a number of deinterlacing methods have been proposed. Recently deinterlacing methods based on neural network have been proposed with good results. On the other hand, with high resolution video contents such as HDTV, the amount of video data to be processed is very large. As a result, the processing time and hardware complexity become an important issue. In this paper, we propose an efficient implementation of neural network deinterlacing using polynomial approximation of the sigmoid function. Experimental results show that these approximations provide equivalent performance with a considerable reduction of complexity. This implementation of neural network deinterlacing can be efficiently incorporated in HW implementation.

Citation Download Citation

Guiwon Seo, Hyunsoo Choi, and Chulhee Lee "Efficient implementation of neural network deinterlacing", Proc. SPIE 7245, Image Processing: Algorithms and Systems VII, 724519 (10 February 2009); https://doi.org/10.1117/12.810571

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