First-order and multi-stage first-order image subsampling using a FANN-based pattern matching method

We propose a method for image subsampling using feedforward artificial neural networks (FANN). Our method employs pattern matching in order to extract information about the presence of local edges. This information is then used to select the FANN desired output values during training. We show that, when applied to first-order and high-order image subsampling, our method effectively subsamples both high detail and smooth image areas, consistently outperforming traditional lowpass filtering and subsampling (LPFS) methods.