Fast and high performance image subsampling using feedforward neural networks.
We introduce a fast and high performance image subsampling method using feedforward artificial neural networks (FANNs). Our method employs a pattern matching technique to extract local edge information within the image, in order to select the FANN desired output values during the supervised training stage. Subjective and objective evaluations of experimental results using still images and video frames show that our method, while less computationally intensive, outperforms the standard lowpass filtering and subsampling method.
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