Quantization level increase in human face images using multilayer neural network
Kenji Nakayama, Y. Kimura, Hayata KATAYAMA · 2005
In this paper, quantization level increase in human face images using a multilayer neural network (NN) is investigated. Basically, it is impossible to increase quality without any other information. However, when images are limited to some category, image restoration could be possible, based on the common properties in this category. The multilayer NN is trained using human face images of 32/spl times/32 pixels with 8-levels as the input data, and 256-level images as the targets. The standard backpropagation algorithm is employed. 20, 40 and 100 training data are examined. By increasing the training data, a general function of regenerating missing information can be achieved. The internal structure of the trained NN is analyzed using some special input images. As a result, it has been confirmed that the NN regards the input image as the human face, and extracts features of the face. The input image is transformed using these features and the common properties of the training data, extracted and held on the connection weights, to the human face image.