Persistent Homology Residual Learning of Deep Convolution Neural Network for Block Match Three Dimension De-Noising Algorithm

Abstract

Deep learning for patch based denoising has done best performance but if an image is comprised of various similar patterns then the performance of these CNNs gets degraded. Persistent homology is a mathematical model based on topological analysis of data method. This paper proposes a novel method to de-noise an image using Persistent homology residual learning for block match three dimension algorithm using a deep residual learning algorithm is used with feature space. The learning incorporated here is mainly for the performance to be improved by the input and tag manifolds which in turn is simpler in terms of topologically for mapping to a feature space. From the experimental conducted and the obtained results its demonstrate the effectiveness of the persistent residual learning in image de-noising for block match three dimension algorithm and it is observed that the proposed algorithm outperforms in terms of elimination of Gaussian noise in images based on performance metric and visual quality.

[Retracted] Optimization of Machine Online Translation System Based on Deep Convolution Neural Network Algorithm

“…The translation output of machine online translation needs to be evaluated by the quality evaluation model [ 12 , 13 ]. Based on the classical recurrent neural networks (NNs), a memory cell structure is proposed.…”