Adaptive autoregressive deinterlacing method

Abstract

This paper proposes a single-field deinterlacing method based on the autoregressive model and edge map. The new method interpolates missing pixels through estimating the deinterlaced covariance from the interlaced covariance, instead of estimating the edge orientations as previous intrafield deinterlaced methods (line average, edge-based lineaverage, direction-oriented interpolation, etc.) do. The proposed method adopts autoregressive mechanism, which considers mutual influence between the estimated missing pixels in a slip window. In addition, adding an edge map in our algorithm is used to reduce the computational complexity. The experimental results show that the proposed method outperformed the previous method in peak signal-to-noise ratio, and common artifacts (serration, line crawl, flicker, blurring, etc.) are significantly reduced. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

Optimally Determined Modified Bayer Color Array for Imagery

“…There have been many interpolation methods [2][3][4][5][6][7][8][9][10]. There are three well-known conventional color interpolation methods: (1) nearest-neighbour interpolation (NNI), (2) bilinear interpolation (BI), and (3) cubic interpolation (CI).…”

Efficient adaptive deinterlacing algorithm with awareness of closeness and similarity

“…One is the intra-field spatial domain method that only uses one field [3][4][5][6][7][8][9][10][11][12][13][14] and the other is an inter-field temporal deinterlacing method that uses multiple fields. One is the intra-field spatial domain method that only uses one field [3][4][5][6][7][8][9][10][11][12][13][14] and the other is an inter-field temporal deinterlacing method that uses multiple fields.…”