A Sparse Reconstruction Approach to Video Deinterlacing
With the apparition of digital television and flat displays, interlaced to progressive frame format conversion represents an importantant video systems feature. In this chapter, we use an inverse problem formulation for video deinterlacing and propose a two-step sparse-reconstruction algorithm for solving it. Firstly, an edge-preserving approximation of the progressive frame is obtained and used for triggering a bidirectional motion-compensated prediction for the current field. In a second step, a sparse residual is calculated as difference between the current field and the projection of its temporal prediction using the same parity sampling matrix. This field residual is further reconstructed using a total-variation regularization method and added back to the motion-compensated prediction to form the final progressive frame. The proposed deinterlacing method presents high quality results compared to other deinterlacing approaches.
KeywordsHigh Quality Result Total Variation Minimization Inverse Optimization Problem Iterative Soft Thresholding Temporal Field Average
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