Optical EngineeringDeinterlacing based on modularization by local frequency characteristics
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We present a new deinterlacing algorithm based on modularization by the local frequency characteristics of images. The input patterns of an image are divided into two regions—the edge region and the smooth region. Each region is assigned to one neural network. The local frequency characteristics of patterns are similar within each region, resulting in more accurate training for each network. The regional neural networks are composed of two modules. One is for the low-frequency components of the input pattern, and the other is for the high-frequency components. Both modules are combined in the output. Therefore, each module compensates for the drawbacks of the other. In simulation, the proposed algorithm showed better performances in both still images and video sequences than other algorithms.