Learned Multi-Field De-Interlacing with Feature Alignment via Deformable Residual Convolution Blocks

Ronglei Ji, A. Murat Tekalp

Abstract: Deinterlacing continues to be an important problem of interest since many digital TV broadcasts and catalog content are still in interlaced format. Although deep learning has had huge impact in all forms of image/video processing, learned deinterlacing has not received much attention in the industry or academia. In this paper, we propose a novel multi-field deinterlacing network that aligns features from adjacent fields to a reference field (to be deinterlaced) using deformable residual convolution blocks. To the best of our knowledge, this paper is the first to propose fusion of multi-field features that are aligned via deformable convolutions for deinterlacing. We demonstrate through extensive experimental results that the proposed method provides state-of-the-art deinterlacing results in terms of both PSNR and perceptual quality.