Residual encoder-decoder up-sampling for structural preservation in noise removal

Abstract

While denoising 3D MRI, structural preservation is a very critical process in the medical region. However, Rician noise in MRI has affected the image quality which is complicated for diagnosing the disease like a brain tumor. So the Rician noise removal process is introduced in this article using filters based method named Residual Encoder-Decoder Up-sampling Non-Similar Wassertein Generative Adversarial Network (REDUPNSWGAN) which preserves the structural similarity between the neighbor-hood slices in 3D configuration is utilized as GPU. Residual auto-encoders are connected with De-convolution processes that are sent to the Generator Net (GNET). Furthermore, to reduce the level of Huber loss, the perceptual loss for similarity is implemented by the extraction of the feature space using Average-pool Net (AVGNET) which is incorporated with Huber loss, signed structural non-similar loss and Wassertein Adversarial loss to form the REDUPNSWGAN loss. Experimentally, this proposed method shows better performance quality based on mean PSNR than REDWGANVGG19 and also time complexity based on training and testing period which shows better than REDWGANVGG19. Particularly, the proposed network Residual Encoder-Decoder Upsampling Non-Similar REDUPNSWGAN reduces the noise level and preserves the structural similarity.