Edge adaptive intra field de-interlacing of video images

Abstract

Expanding image by an arbitrary scale factor and thereby creating an enlarged image is a crucial image processing operation. De-interlacing is an example of such operation where a video field is enlarged in vertical direction with 1 to 2 scale factor. The most advanced de-interlacing algorithms use a few consequent input fields to generate one output frame. In order to save hardware resources in video processors, missing lines in each field may be generated without reference to the other fields. Line doubling, known as "bobbing", is the simplest intra field de-interlacing method. However, it may generate visual artifacts. For example, interpolation of an inserted line from a few neighboring lines by vertical filter may produce such visual artifacts as "jaggies". In this work we present edge adaptive image up-scaling and/or enhancement algorithm, which can produce "jaggies" free video output frames. As a first step, an edge and its parameters in each interpolated pixel are detected from gradient squared tensor based on local signal variances. Then, according to the edge parameters including orientation, anisotropy and variance strength, the algorithm determines footprint and frequency response of two-dimensional interpolation filter for the output pixel. Filter's coefficients are defined by edge parameters, so that quality of the output frame is controlled by local content. The proposed method may be used for image enlargement or enhancement (for example, anti-aliasing without resampling). It has been hardware implemented in video display processor for intra field de-interlacing of video images.

High-performance image deinterlacing using optical flow and artifact post-processing on GPU/CPU for surveillance and reconnaissance tasks

“…• Block matching based motion estimation 11,[18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] • Edge direction dependent block based interpolation [35][36][37][38][39][40][41] • Edge direction dependent interpolation for example with adaptive distance weighting or based on second order image derivatives [42][43][44][45][46] • Gradient analysis with covariance matrices or tensors 47,48 • Using adaptive NL-means, bilateral or trilateral filters [49][50][51][52][53] • Dynamic time warping 54…”