1 research outputs found
On the Adaptability of Neural Network Image Super-Resolution
In this paper, we described and developed a framework for Multilayer
Perceptron (MLP) to work on low level image processing, where MLP will be used
to perform image super-resolution. Meanwhile, MLP are trained with different
types of images from various categories, hence analyse the behaviour and
performance of the neural network. The tests are carried out using qualitative
test, in which Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and
Structural Similarity Index (SSIM). The results showed that MLP trained with
single image category can perform reasonably well compared to methods proposed
by other researchers.Comment: Image Super Resolution, Neural Network, Multilayer Perceptron, Mean
Squared Error, Peak Signal-to-Noise Ratio, Structural Similarity Inde