Details

Autor(en) / Beteiligte

• Suryanarayana, Gunnam
• Chandran, Karthik
• Khalaf, Osamah Ibrahim
• Alotaibi, Youseef
• Alsufyani, Abdulmajeed
• Alghamdi, Saleh Ahmed

Titel

Accurate Magnetic Resonance Image Super-Resolution Using Deep Networks and Gaussian Filtering in the Stationary Wavelet Domain

Ist Teil von

• IEEE access, 2021, Vol.9, p.71406-71417

Ort / Verlag

Piscataway: IEEE

Erscheinungsjahr

2021

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• In this correspondence, we present an accurate Magnetic Resonance (MR) image Super-Resolution (SR) method that uses a Very Deep Residual network (VDR-net) in the training phase. By applying 2D Stationary Wavelet Transform (SWT), we decompose each Low Resolution (LR)-High Resolution (HR) example image pair into its low-frequency and high-frequency subbands. These LR-HR subbands are used to train the VDR-net through the input and output channels. The trained parameters are then used to generate residual subbands of a given LR test image. The obtained residuals are added with their LR subbands to produce the SR subbands. Finally, we attempt to maintain the intrinsic structure of images by implementing the Gaussian edge-preservation step on the SR subbands. Our extensive experimental results show that the proposed MR-SR method outperforms the existing methods in terms of four different objective metrics and subjective quality.