The marked region in the first column demonstrates the networks ability to process the artifacts caused by out-of-focus particles within the sample. A pixel-wise registration must be performed to ensure the success of the network in learning the transformation to perform super-resolution. These were estimated using an autofocusing algorithm. This pixel-limited resolution of an on-chip holographic microscope is a result of its unit magnification. To develop and evaluate a novel and generalizable super-resolution (SR) deep-learning framework for motion-compensated isotropic 3D coronary MR angiography (CMRA), which allows free-breathing acquisitions in less than a minute. Google Scholar. implemented the network. The sample is a Massons trichrome stained lung tissue slide, imaged at an illumination wavelength of 550nm. Bethesda, MD 20894, Web Policies We have presented a GAN-based framework that can super-resolve images taken using both pixel size limited and diffraction limited coherent imaging systems. In Advances in Neural Information Processing Systems 27 (eds Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D. & Weinberger, K. Sparsity-based multi-height phase recovery in holographic microscopy. While the first fully connected layer did not change the dimensionality, the second reduced the output of each patch to a single number which was in turn input into a sigmoid function. X.Z. Greenbaum, A. Make sure your setModel() parameters matches with the model you are using in readModel(). Lett. A generator network is used to generate an image that has the same features as the label (ground truth) image, and a discriminator network tries to distinguish between the generated and label (ground truth) images. 2, which summarizes both the hologram reconstruction procedure as well as the image super-resolving technique with and without using the network. Visualized result for the diffraction-limited system. 7, the higher spatial frequencies of the network output image approach the spatial frequencies observed in the ground truth images. Lett., OL 22, 12681270 (1997). -. Visualized result for the pixel size-limited system. At the input of each block, the previous output was up-sampled using a bilinear interpolation and concatenated with the output of the down-sampling path at the same level (see Fig. & Ozcan, A. Propagation phasor approach for holographic image reconstruction. At the output of the second convolution of each block the number of channels was doubled. . deep-learning pytorch gan super-resolution image-restoration face-restoration gfpgan Updated Oct 23, 2022; Python . Crop F, Guillaud O, Ben Haj Amor M, Gaignierre A, Barre C, Fayard C, Vandendorpe B, Lodyga K, Mouttet-Audouard R, Mirabel X. Phys Imaging Radiat Oncol. Nature Methods 16, 103110 (2019). official website and that any information you provide is encrypted 7, the higher spatial frequencies of the network output image approach the spatial frequencies observed in the ground truth images. The mapping is represented as a deep convolutional neural network (CNN) [15] that takes the lowresolution image as the input and outputs the high-resolution one. there are many deep learning based image super-resolution methods such as super-resolution using deep convolutional neural networks (srcnn) (dong et al. A classical solution to this missing phase problem is given by off-axis holography3,4, which in general results in a reduction of the space-bandwidth product of the imaging system. Each of these vectors were subsequently fed into two fully connected layers and LReLU activation layers in series. Bustin A, Ginami G, Cruz G, Correia T, Ismail TF, Rashid I, Neji R, Botnar RM, Prieto C. Magn Reson Med. 9. T.L. The real and imaginary components of the phase recovered image pairs were used to train the network. Results: Medical images play an important role in medical diagnosis and research. Lets look at C++ first: I will explain the important parts of the code. Unable to load your collection due to an error, Unable to load your delegates due to an error, A proposed residual block consists of a sequence of channel widening, convolution followed by activation, channel squeeze and excitation (SE) block. government site. By submitting a comment you agree to abide by our Terms and Community Guidelines. Average SSIM values for the lung and Pap smear samples for the deep neural network output (also see Figs5 and and66 for sample images in each category). The down-sampling blocks were connected by an average-pooling layer of stride two that down-samples the output of the previous block by a factor of two in both lateral dimensions (see Fig. The https:// ensures that you are connecting to the Note that cubic spline interpolation creates unnecessary amplification and attenuation of flow where SRflow (opt) preserves the flow intensity closest to the reference (HR). A.O. The corresponding lower resolution phase images are then matched to this larger image. J. Biomech. The FOV of each tissue image was ~20 mm2 (corresponding to the sensor active area). In this case only the phase channel was used to train the network. The proposed approach is not restricted to a specific coherent imaging modality and is broadly applicable to various coherent image formation techniques. This is done by creating a correlation score matrix between the stitched high resolution image and each of the lower resolution images. 10.1371/journal.pone.0188323 Nat. how many times you will increase the resolution. Deep learning-based super-resolution in coherent imaging systems. Copyright 2022 Shit, Zimmermann, Ezhov, Paetzold, Sanches, Pirkl and Menze. Schematic of the training process for deep learning-based optical super-resolution for an NA-limited coherent imaging system. Before Federal government websites often end in .gov or .mil. Rivenson, Y., Zhang, Y., Gunaydin, H., Teng, D. & Ozcan, A. Ronneberger, O., Fischer, P. & Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. Sparsity-based multi-height phase recovery in holographic microscopy. Article Radiology. For the pixel-super-resolution network (System A), the network training process is demonstrated in Fig. They only show a very small increase from a value of 0.876 for the input image to 0.879 for the network output. & Zhang, T. Phase-shifting digital holography. refer to the discriminator and generator network operators, respectively, xinput is the lower resolution input to the generator, and zlabel is the higher resolution label image. PMC Image super-resolution is a one-to-many problem, but most deep-learning based methods only provide one single solution to this problem. 2011;4:50-61. Image quality assessment: from error visibility to structural similarity. OpenCV is an open-source computer vision library that has an extensive collection of great algorithms. To obtain Comparison of the performances for the deep-learning-based pixel super-resolution methods using different input images. An outline of the data required to generate the network input and ground truth images is shown, together with an overview of how both the traditional super-resolution is performed and how the deep learning super-resolution network is trained. proposed the network structure. This layer was used to increase the number of channels to 32 and was followed by five discriminator blocks all containing two convolutional layers activated upon by LReLU functions. All the networks were trained with a batch size of 10 using 128128 pixel patches. For the diffraction-limited super-resolution network (System B), the network training process was demonstrated in Fig. For each iteration that the discriminator is updated, the generator network is updated four times, which helps the discriminator avoid overfitting to the target images. The image super-resolution reconstruction algorithm based on residual model can increase the number of network layers and improve network performance, but it has the problem of large network scale. Would you like email updates of new search results? Berhane H, Scott MB, Barker AJ, McCarthy P, Avery R, Allen B, Malaisrie C, Robinson JD, Rigsby CK, Markl M. Magn Reson Med. Scientific Reports 6, 22738 (2016). The number of training steps as well as the training time for each network are reported in Table1, and the testing times are reported in Table2. Bookshelf For more information and implementations of these models see the modules GitHub README file. 3. Finally, each image was cropped by 50 pixels to each side to accommodate for any relative shift that may have occurred. MRI super-resolution reconstruction for MRI-guided adaptive radiotherapy using cascaded deep learning: In the presence of limited training data and unknown translation model. Deep learning-based super-resolution in coherent imaging systems, $${\rm{\Delta }}{{L}}_{c}\approx \sqrt{\frac{2\,\mathrm{ln}\,2}{\pi }}\cdot \frac{{\lambda }^{2}}{n{\rm{\Delta }}\lambda }={\rm{100.47}}\,\mu {\rm{m}}$$, $${\rm{NA}}=n\,\sin \,\theta =n\sqrt{1-{\cos }^{2}\theta }=n\sqrt{1-{(\frac{{z}_{2}}{{z}_{2}+{\rm{\Delta }}{L}_{c}})}^{2}}\approx 0.6624$$, $$d\propto \frac{\lambda }{{\rm{N}}{\rm{A}}}=\frac{0.55}{0.6624}={\rm{0.8303}}\,\mu {\rm{m}}$$, $${l}_{{\rm{d}}{\rm{i}}{\rm{s}}{\rm{c}}{\rm{r}}{\rm{i}}{\rm{m}}{\rm{i}}{\rm{n}}{\rm{a}}{\rm{t}}{\rm{o}}{\rm{r}}}=D{(G({x}_{{\rm{i}}{\rm{n}}{\rm{p}}{\rm{u}}{\rm{t}}}))}^{2}+{(1-D({z}_{{\rm{l}}{\rm{a}}{\rm{b}}{\rm{e}}{\rm{l}}}))}^{2}$$, $${l}_{{\rm{g}}{\rm{e}}{\rm{n}}{\rm{e}}{\rm{r}}{\rm{a}}{\rm{t}}{\rm{o}}{\rm{r}}}={L}_{1}\{{z}_{{\rm{l}}{\rm{a}}{\rm{b}}{\rm{e}}{\rm{l}}},G({x}_{{\rm{i}}{\rm{n}}{\rm{p}}{\rm{u}}{\rm{t}}})\}+\gamma \times TV\{G({x}_{{\rm{i}}{\rm{n}}{\rm{p}}{\rm{u}}{\rm{t}}})\}+\alpha \times {(1-D(G({x}_{{\rm{i}}{\rm{n}}{\rm{p}}{\rm{u}}{\rm{t}}})))}^{2}$$, $${L}_{1}\{{z}_{{\rm{label}}},G({x}_{{\rm{input}}})\}={{\rm{E}}}_{n\_\mathrm{pixels}}({{\rm{E}}}_{n\_\mathrm{channels}}(|G({x}_{{\rm{input}}})-{z}_{{\rm{label}}}|))$$, $$TV={E}_{n\_\mathrm{channels}}({\sum }_{i,j}|G{({x}_{{\rm{input}}})}_{i+1,j}-G{({x}_{{\rm{input}}})}_{i,j}|+|G{({x}_{{\rm{input}}})}_{i,j+1}-G{({x}_{{\rm{input}}})}_{i,j}|)$$, $${l}_{{\rm{g}}{\rm{e}}{\rm{n}}{\rm{e}}{\rm{r}}{\rm{a}}{\rm{t}}{\rm{o}}{\rm{r}}}={L}_{1}\{{z}_{{\rm{l}}{\rm{a}}{\rm{b}}{\rm{e}}{\rm{l}}},G({x}_{{\rm{i}}{\rm{n}}{\rm{p}}{\rm{u}}{\rm{t}}})\}+\gamma \times TV\{G({x}_{{\rm{i}}{\rm{n}}{\rm{p}}{\rm{u}}{\rm{t}}})\}+\alpha \times {(1-D(G({x}_{{\rm{i}}{\rm{n}}{\rm{p}}{\rm{u}}{\rm{t}}})))}^{2}\,+\beta \times {\rm{S}}{\rm{S}}{\rm{I}}{\rm{M}}\{G({x}_{{\rm{i}}{\rm{n}}{\rm{p}}{\rm{u}}{\rm{t}}}),{z}_{{\rm{l}}{\rm{a}}{\rm{b}}{\rm{e}}{\rm{l}}}\}$$, $${\rm{SSIM}}(x,z)=\frac{(2{\mu }_{x}{\mu }_{z}+{c}_{1})(2{\sigma }_{x,z}+{c}_{2})}{({\mu }_{x}^{2}+{\mu }_{z}^{2}+{c}_{1})({\sigma }_{x}^{2}+{\sigma }_{z}^{2}+{c}_{2})}$$, \({\sigma }_{x}^{2},\,{\sigma }_{z}^{2}\), $${\rm{L}}{\rm{R}}{\rm{e}}{\rm{L}}{\rm{U}}\,(x)=\{\begin{array}{ll}x & {\rm{for}}\,x > 0\\ 0.1x & {\rm{otherwise}}\end{array}$$, https://doi.org/10.1038/s41598-019-40554-1. Wide-field computational imaging of pathology slides using lens-free on-chip microscopy. Ouyang, W., Aristov, A., Lelek, M., Hao, X. SR-CMRA showed statistically significant (P < .001) improved vessel sharpness 34.1% 12.3% and length 41.5% 8.1% compared with LR-CMRA. AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos: AnimeSR: arxiv- . 8600 Rockville Pike JACC Cardiovasc Imaging. arXiv:1505.04597 [cs] (2015). Based on eq. Am. SRGAN was evaluated on a retrospective downsampled cohort of 50 patients and on 16 prospective patients that were scanned with LR-CMRA in ~50 s under free-breathing. A multi-scale variational neural network for accelerating motion-compensated whole-heart 3D coronary MR angiography. The loss function for the lens-based coherent microscope images (System B) incorporated an additional structural similarity index (SSIM)32 term in addition to the terms included for the lensfree on-chip imaging system, i.e. Optica 5, 704710 (2018). Next, the average pooling layer was used to find the mean of each channel, reducing the dimensionality to a vector of length 1024 for each patch. (a) A Lens-free on-chip holographic microscope. Vessel sharpness and length of the coronary arteries from the SR-CMRA is compared against the HR-CMRA. 4b). Another indication that the super-resolution is successful is that the higher spatial frequency components in the output of the network are very close to the spatial frequencies of the ground truth image. Y.R. TV{G(xinput)} represents the total variation loss, which acts as a regularization term, applied to the generator output. An official website of the United States government. The number of training steps as well as the training time for each network are reported in Table1, and the testing times are reported in Table2. Development and validation of a deep learning-based laparoscopic system for improving video quality, Adaptive 3D descattering with a dynamic synthesis network, DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning, Deep learning-based hologram generation using a white light source, Computational cytometer based on magnetically modulated coherent imaging and deep learning, Deep learning in holography and coherent imaging, Emerging role of machine learning in light-matter interaction, https://doi.org/10.1038/s41377-019-0139-9, https://doi.org/10.1021/acsphotonics.8b00146, http://creativecommons.org/licenses/by/4.0/. As in the pixel super-resolution case reported earlier, two samples were obtained from two different patients, and the trained network was blindly tested on a third sample obtained from a third patient. For the lung tissue samples, three tissue sections from different patients were used for training. The term SSIM{G(xinput),zlabel} was set to make up ~15% of the total generator loss, with the rest of the regularization weights reduced in value accordingly. As illustrated in Fig. Our generator network used an adapted U-net architecture40. Deep learning-based velocity antialiasing of 4D-flow MRI. conducted the experiments and prepared the training and testing datasets for the network. 6) using three different deep networks, where the input images for each network used a different pixel size (2.24m, 1.12m, and 0.7467m, corresponding to 11, 22 and 33 lateral shifts, respectively, as detailed in the Methods section). To demonstrate that this super-resolution framework can also be applied to a diffraction-limited coherent imaging system, we trained another network using the same architecture (see the Methods section) with images taken from a Pap smear sample. The discriminator loss term, (1 D(G(xinput)))2, made up the remainder of the overall generator loss. For this step, the higher resolution phase images was first stitched together, by calculating the overlap between neighboring images, and using this to stitch them together into a larger image. The results clearly demonstrate the improved structural similarity of the network output images. we teach the network that the HR. The average SSIM values for the entire image FOV (~20 mm2) are listed in Table3, where the input SSIM values were calculated between the bicubic interpolated lower resolution input images and the ground truth images. We propose a deep convolutional neural network (CNN) that learns the inter-scale relationship of the velocity vector map and leverages an efficient residual learning scheme to make it computationally feasible. Greenbaum A, Ozcan A. Maskless imaging of dense samples using pixel super-resolution based multi-height lensfree on-chip microscopy. The network was developed using a desktop computer running the Windows 10 operating system. Artificial Intelligence in Cardiac MRI: Is Clinical Adoption Forthcoming? We quantify our results using the structural similarity index (SSIM)32 and spatial frequency content of the networks output images in comparison to the higher resolution images (which constitute our ground truth). Rivenson, Y., Shalev, M. A. Once the high and low resolution image pairs were accurately registered, they were cropped into smaller image patches (128128 pixels), which were used to train the network. Second, an . The discriminator loss term, (1 D(G(xinput)))2, made up the remainder of the overall generator loss. Internet Explorer). eCollection 2022. Total variation (TV) is defined as: where the i and j indices represent the location of the pixels within each channel of the image. They differ in accuracy, size and speed. A batch size of 10 is used for the training, and a batch size of 25 is used for validation. This registration process correlated the spatial patterns of the phase images and used the correlation to establish an affine transform matrix. Accelerating the Super-Resolution Convolutional Neural Network, in Proceedings of European Conference on Computer Vision ECCV 2016. This loads all the variables of the chosen model and prepares the neural network for inference. Clipboard, Search History, and several other advanced features are temporarily unavailable. Sparse signal recovery methods employed in coherent imaging are based on iterative optimization algorithms. Paturzo M, et al. This data-driven image super-resolution framework is applicable to enhance the performance of various coherent imaging systems. Figure5 illustrates the networks super-resolved output images along with pixel-size limited lower resolution input images and the higher resolution ground truth images of a Pap smear sample. The L1{zlabel,G(xinput)} term is calculated using: This finds the absolute difference between each pixel of the generator output image and its corresponding label. Nature Biotechnology, https://doi.org/10.1038/nbt.4106 (2018). Annu Int Conf IEEE Eng Med Biol Soc. This layer was used to increase the number of channels to 32 and was followed by five discriminator blocks all containing two convolutional layers activated upon by LReLU functions. The seed points are, MeSH Bethesda, MD 20894, Web Policies The essence of the SR algorithm is the mapping function between the low-resolution and high-resolution data. The ground truth (target) image for each SSIM value is acquired using 66 lensfree holograms per height. Careers. Whichever portion of the matrix has the highest correlation score is used to determine which portion of the fused image is cropped out and is used as the input for the network. The last term in eq. Elias Nehme, Daniel Freedman, Yoav Shechtman, Taesik Go, Sangseung Lee, Sang Joon Lee, Yibo Zhang, Mengxing Ouyang, Aydogan Ozcan, Jiajia Zhou, Bolong Huang, Jean-Claude G. Bnzli, Scientific Reports Bishara W, Su T-W, Coskun AF, Ozcan A. Lensfree on-chip microscopy over a wide field-of-view using pixel super-resolution. T.L. The SSIM values for this system do not reveal as large of a trend as was observed for the lensfree on-chip microscopy system reported earlier. 2019 Jan;81(1):102-115. doi: 10.1002/mrm.27354. & Pentland, A. P. A Bayesian computer vision system for modeling human interactions. The SSIM values for this system do not reveal as large of a trend as was observed for the lensfree on-chip microscopy system reported earlier. FOIA 4D-flow MRI; cerebrovascular flow; flow quantification; flow super-resolution; residual learning. Qualitative images from different methods on a representative cerebrovascular sample. The super-resolution (SR) technique, aiming at recovering higher spatial resolution of digital images from lower-resolution observations, has been come up with since the 1980s [ 25 ]. (b) A lens-based in-line holographic microscope, implemented by removing the condenser and switching the illumination to a partially-coherent light source on a conventional bright-field microscope. 2011;124:416-424. To improve the quality of NKFs without additional information during decoding, we use motion estimation to find the most similar blocks between the upscaled NKFs and the associated high-resolution KFs. Once an accurate shift table was estimated, a shift-and-add based pixel super-resolution algorithm33 was applied. 2022 Jul;2022:1707-1710. doi: 10.1109/EMBC48229.2022.9871783. For the lensfree holographic imaging system (System A), the generator loss function was defined by: Diagram of the GAN structure. Fournier C, et al. Several approaches have been demonstrated to improve the resolution of coherent imaging systems15,16,17,18,19,20. doi: 10.1002/cnm.3381. 5, e16060 (2016). The Impacts of COVID-19 on the Achievement Gap, Evaluating Performance and Correctness: VictoriaMetrics response, //Set the desired model and scale to get correct pre- and post-processing. Now we are ready to upscale our own images or even video. Our generator network used an adapted U-net architecture40. The iterative multi-height phase recovery technique34 was applied to eliminate the holographic image artifacts (twin image and self-interference terms4) in both of the coherent imaging systems that were used in this work. The desktop uses an Nvidia GTX 1080 Ti GPU, a Core i7-7900K CPU running at 3.3GHz, and 64 GB of RAM. Super-resolution in digital holography by a two-dimensional dynamic phase grating. MeSH This deep learning-based super-resolution framework can be broadly applied to enhance the space-bandwidth product of coherent imaging systems using image data and convolutional neural networks, and provides a rapid, non-iterative method for solving inverse image reconstruction or enhancement problems in optics. The .gov means its official. Rivenson, Y. et al. The lens-based design has several optical components and surfaces within the optical beam path, making it susceptible to coherence induced background noise and related image artifacts, which can affect the SSIM calculations.
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