  In the present paper, an effort has been made for storing and recalling
images with Hopfield Neural Network Model of auto-associative memory. Images
are stored by calculating a corresponding weight matrix. Thereafter, starting
from an arbitrary configuration, the memory will settle on exactly that stored
image, which is nearest to the starting configuration in terms of Hamming
distance. Thus given an incomplete or corrupted version of a stored image, the
network is able to recall the corresponding original image. The storing of the
objects has been performed according to the Hopfield algorithm explained below.
Once the net has completely learnt this set of input patterns, a set of testing
patterns containing degraded images will be given to the net. Then the Hopfield
net will tend to recall the closest matching pattern for the given degraded
image. The simulated results show that Hopfield model is the best for storing
and recalling images.
