Enhancing The Resolution Of Micro Ct Images Using Super Resolution Convolutional Neural Networks

By healtycares On Aug 26, 2025

Super Resolution Recurrent Convolutional Neural Networks For Learning Single image super resolution (sisr) techniques based on super resolution convolutional neural networks (srcnn) are applied to micro computed tomography ( {\mu}ct) images of sandstone and carbonate rocks. Super resolution algorithms can be used to artificially enhance the resolution of micro ct images to obtain higher resolution images with sufficiently large field of views.

Pdf Application Of Super Resolution Convolutional Neural Network For

Pdf Application Of Super Resolution Convolutional Neural Network For Single image super resolution (sisr) techniques based on super resolution convolutional neural networks (srcnn) are applied to micro computed tomography ( {\mu}ct) images of. These results indicate that the srcnn scheme significantly outperforms the linear interpolation methods for enhancing image resolution in chest ct images. the results also suggest that srcnn may become a potential solution for generating high resolution ct images from standard ct images. Our main contribution is the adjustment of the 8x super resolution algorithm for the segmented 3d micro ct images of rocks. in this example, we prepared the training dataset and tuned the model for berea sandstone. below, we demonstrate the performance of the model. The practical application of computed tomography (ct) faces the dilemma between higher image resolution and less x ray exposure for patients, motivating the res.

Pdf Image Super Resolution For Mri Images Using 3d Faster Super Our main contribution is the adjustment of the 8x super resolution algorithm for the segmented 3d micro ct images of rocks. in this example, we prepared the training dataset and tuned the model for berea sandstone. below, we demonstrate the performance of the model. The practical application of computed tomography (ct) faces the dilemma between higher image resolution and less x ray exposure for patients, motivating the res. This study is undertaken to compare different state of the art cnn based super resolution algorithms to enhance the resolution of micro ct images of heterogeneous carbonate rocks. We develop a procedure for substantially improving the quality of segmented 3d micro computed tomography (micro ct) images of rocks with a machine learning (ml) generative model. Experimental results demonstrate that our method outperforms the state of the art super resolution methods on restoring ct images with respect to peak signal to noise ratio (psnr), structural similarity (ssim) and normalized root mean square error (nrmse) indices. In this research, a super resolution technique employing the u net 3d cnn architecture is applied to enhance the resolution of granodiorite rock sample images obtained by two diferent 3d scanning machines. the high resolution dataset was obtained using the xradia versa xrm 500 microscope.

Super Resolution Model Based Iterative Reconstruction For Lens Coupled This study is undertaken to compare different state of the art cnn based super resolution algorithms to enhance the resolution of micro ct images of heterogeneous carbonate rocks. We develop a procedure for substantially improving the quality of segmented 3d micro computed tomography (micro ct) images of rocks with a machine learning (ml) generative model. Experimental results demonstrate that our method outperforms the state of the art super resolution methods on restoring ct images with respect to peak signal to noise ratio (psnr), structural similarity (ssim) and normalized root mean square error (nrmse) indices. In this research, a super resolution technique employing the u net 3d cnn architecture is applied to enhance the resolution of granodiorite rock sample images obtained by two diferent 3d scanning machines. the high resolution dataset was obtained using the xradia versa xrm 500 microscope.

Super Resolution Convolutional Neural Network Models For Enhancing Experimental results demonstrate that our method outperforms the state of the art super resolution methods on restoring ct images with respect to peak signal to noise ratio (psnr), structural similarity (ssim) and normalized root mean square error (nrmse) indices. In this research, a super resolution technique employing the u net 3d cnn architecture is applied to enhance the resolution of granodiorite rock sample images obtained by two diferent 3d scanning machines. the high resolution dataset was obtained using the xradia versa xrm 500 microscope.

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Enhancing the Resolution of Micro-CT Images Using Super-Resolution Convolutional Neural Networks

Enhancing the Resolution of Micro-CT Images Using Super-Resolution Convolutional Neural Networks

Enhancing the Resolution of Micro-CT Images Using Super-Resolution Convolutional Neural Networks Deeply-Recursive Convolutional Network for Image Super-Resolution Super-Resolution: Computational and Deep Learning-Based Approaches 𝗙𝗮𝘀𝘁 𝗦𝘂𝗽𝗲𝗿-𝗥𝗲𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗖𝗼𝗻𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗮𝗹 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 (𝗙𝗦𝗥𝗖𝗡𝗡) Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution Medical Image Super-resolution/Translation Warning! Multimodal Multi-Head Conv Attention with Various Kernel Sizes for Medical Image Super-Resolution Super-resolution of multispectral satellite images using convolutional neural networks Deep Learning For Image 'Enhancement' | Super-Resolution Series #2 - Concept Review Image Processing | Part-6 | Implementation : Super-Resolution Convolutional Neural Network (SRCNN) Image Quality Assessment using Convolutional Neural Network in Clinical Skin Images Deep learning for medical image reconstruction, super-resolution, classification and segmentation Super Resolution Imaging with Integrated CLEM Armin Mehri - Single Image Super-Resolution: Towards a Multispectral Scheme Image Super-Resolution With Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining Learning Texture Transformer Network for Image Super-Resolution Super-resolution Imaging of Neuronal DCVs | Protocol Preview WACV18: Cascade Trained and Trimmed Deep Convolution Neural Networks for Image Super Resolution Accurate Image Super-Resolution Using Very Deep Convolutional Networks Multi-view Appearance Super-resolution with CNNs | JRC Workshop 2021

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