URS-Nets++ : unified dense residual networks with multi-headed attention layers for removal of EMI noise from CT images

U-Nets occupy the major share in the removal of EMI noises from the CT Images. However, lack of network depth leads to performance degradation in existing variants of U-Nets which remains to be the hardest challenge to remove the EMI noises from the CT scan images. In this paper, a modified U-Nets (URS-Nets++) framework named multi-Headed Attention Based Residual Convolutional Block (MHA-RCB) for the EMI- based Image denoising is proposed. The improvisations in the existing U-Nets are as follows: (1) URS-Nets++ uses the novel convolutional residual networks (CRN) to deepen the network depth, to avoid the network degradation performance. (2) The proposed network introduces the depth-wise attention layers in the convolutional residual network to improve the up-sampling and down-sampling process (3) The proposed URS-Nets++ introduces the modified skip connections (MSP) which are used to fuse the shallow feature information into deeper images details and provides the strong path to obtain the clean images. Extensive experimentation has been conducted for the handcrafted datasets and metrics such as SSIM, PSNR, IoU, DICE are evaluated and analyzed. Ablation study is presented based on original noisy image dataset and standard datasets. Moreover, the performance metrics of the proposed algorithm is compared with the other state-of-the-art deep learning architecture and U-Nets' variants. Results demonstrate that the EMI denoising technique designed by the proposed network yields smoother and sharper images than the other existing methods with better visual quality.