Table 1.
Identifying gaps in state-of-the-art models compared to the proposed method.
Fig 1.
Workflow of the proposed end-to-end architecture, including downsampling, implicit neural representation (INR), and super-resolution (SR) modules.
Fig 2.
Illustration of the data structure in the context of the metrics, PSNR, SSIM and CR, according to the DS, NC and SN dimensions.
Table 2.
Performance of the INR module and the whole end-to-end architecture.
(The upper row shows the performance of a single SIREN and the lower row shows that of the whole end-to-end architecture).
Table 3.
Average values for different INR layers and neurons.
Table 4.
The results of our proposed architecture with 4 channels of shallow feature extractor in SR module.
Table 5.
The results of our proposed network with 8 channels of shallow feature extractor in SR module.
Table 6.
The results of our proposed network with 16 channels of shallow feature extractor in SR module.
Table 7.
Our proposed architecture’s trade-off point.
Fig 3.
Illustrates the trade-off point for the number of channels (NC) in the SR module concerning the performance metrics, 1/PSNR, 1-SSIM, and 1-CR.
The red dashed lines indicate the intersection where the optimal trade-off is achieved, balancing compression efficiency and reconstruction quality.
Fig 4.
The trade-off point for the downsampling scale (DS) is based on the performance metrics, 1/PSNR, 1-SSIM, and 1-CR.
The red dashed lines highlight where the downsampling scale achieves an optimal balance between compression rate and reconstruction accuracy.
Fig 5.
The trade-off point for the number of neurons (SN) in the SIREN model, plotted against the performance metrics, 1/PSNR, 1-SSIM, and 1-CR.
The red dashed lines indicate the optimal configuration of neurons in the SIREN model for achieving high reconstruction quality with minimal compression loss.
Fig 6.
The Left column shows the different original slices of the volume with sizes of (155, 240, 240); the middle column shows the labelled patches of the slices with sizes of (64, 64, 64); the right column shows the reconstructed patches by our architecture.
Fig 7.
Training procedure of the architecture according to the trade-off point setting.
Table 8.
Comparison of our techniques with other state-of-the-art methods in terms of PSNR and SSIM in volume reconstruction.