  Image-matched nonseparable wavelets can find potential use in many
applications including image classification, segmen- tation, compressive
sensing, etc. This paper proposes a novel design methodology that utilizes
convolutional neural net- work (CNN) to design two-channel non-separable
wavelet matched to a given image. The design is proposed on quin- cunx lattice.
The loss function of the convolutional neural network is setup with total
squared error between the given input image to CNN and the reconstructed image
at the output of CNN, leading to perfect reconstruction at the end of train-
ing. Simulation results have been shown on some standard images.
