Analysis of Image Storage and Retrieval in Graded Memory

An approach to storing and retrieving static images using multilayer Hopfield neural network is analyzed. Here, the Hopfield network is used as a memory, which stores images in predefined resolution. During the image retrieval, down sampled version of the stored image is provided as the query mage, The memory initially gives out a coarse image. The finer details of the image are synthesized later by using this coarse output image. This coarse output image is fed as the input to the memory again. The output this time will be better than the output that was got initially. The output of the memory becomes better and better as the time progresses. We call this memory a graded memory. Here the work proposes various models of the graded memory using multilayer Hopfield neural network, analyses the effectiveness of this memory with parameters like MSE, RMSE and PSNR.