Pattern learning based image restoration using neural networks

This paper presents a generic pattern learning based image restoration scheme for degraded digital images, where a feed-forward neural network is employed for implementation of the proposed techniques. The methodology reported in this paper can be applied in different circumstances, for instance, quality enhancement as a post-processing of image compression schemes, blur image restoration and noise image filter, provided that the training data set is comprised of patterns rich enough for supervised learning, This paper focuses on the problem of coded image restoration. The key points addressed in this work are (1) the use of edge information extracted from source images as a priori knowledge in the regularization function to recover the details and reduce the ringing artifact of the coded images; (2) the theoretic basis of the pattern learning-based approach using implicit function theorem; (3) subjective quality enhancement with the use of an image similarity for training neural networks; and (4) empirical studies with comparisons to the set partitioning in hierarchical tree (SPIHT) method. The main merits of this model-based neural image restoration approach include strong robustness with respect to transmission noise and the parallel processing for real-time applications. The experimental results demonstrate promising performance on both objective and subjective quality for lower compression ratio subband coded images