deep learning based super resolution

Subject terms: Computational science, Magnetic resonance imaging IEEE Transactions on Image Processing 13, 600612 (2004). Radially-averaged spatial frequency spectra of the network input, network output and target images, corresponding to a lens-based coherent imaging system. The site is secure. 10.1016/j.jbiomech.2017.05.004 The architectures of the generator (G) and the discriminator (D) that make up the GAN can be seen in Fig. Bukka, R. Gupta, A. R. Magee, and R. K. Jaiman, " Assessment of unsteady flow predictions using hybrid deep learning based reduced-order models," Phys. Liu, Y., Tian, L., Hsieh, C.-H. & Barbastathis, G. Compressive holographic two-dimensional localization with 1/302 subpixel accuracy. At the output of the second convolution of each block the number of channels was doubled. See this image and copyright information in PMC. For the lens-based diffraction-limited coherent imaging system (System B), the autofocusing algorithm required an additional background subtraction step. Accessibility 3,364 Highly Influential PDF View 5 excerpts, references methods This data-driven image super-resolution framework is applicable to enhance the performance of various coherent imaging systems. The filter size for each convolution was set to be 33. Before being fed into the network, an image needs to be upsampled via bicubic interpolation. Manuscript related data can be requested from the corresponding author. For undesired particles or dust associated with the objective lens or other parts of the optical microscope, the diffraction pattern that is formed is independent of the sample and its position. 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. Image super-resolution is a one-to-many problem, but most deep-learning based methods only provide one single solution to this problem. [2] Shi, W., Caballero, J., Huszr, F., Totz, J., Aitken, A., Bishop, R., Rueckert, D. and Wang, Z., Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network, Proceedings of the IEEE conference on computer vision and pattern recognition CVPR 2016. A.O. Qualitative comparison of streamlines obtained from different methods on two representative, Bland-Altman plot for the three velocity components of 50,000 random samples from the. Nature Methods 16, 103110 (2019). Visualized result for the diffraction-limited system. You can choose between: edsr, fsrcnn, lapsrn, espcn. We present a detailed comparative study between the proposed super-resolution and the conventional cubic B-spline based vector-field super-resolution. Science Translational Medicine 6, 267ra175267ra175 (2014). The FOV of each tissue image was ~20 mm2 (corresponding to the sensor active area). and A.O. Ouyang, W., Aristov, A., Lelek, M., Hao, X. Prospective navigator correction of image position for coronary MR angiography. Hamdan A, Asbach P, Wellnhofer E, et al. Following this, a correlation-based registration, which corrected any rotational misalignments or shifts between the images was performed. Schematic of the training process for deep-learning based pixel super-resolution. Tairan Liu, Kevin de Haan, Yair Rivenson and Zhensong Wei contributed equally. 3. The networks were blindly tested on additional tissue sections from other patients. For the diffraction-limited coherent imaging system (System B), an additional rough FOV matching step was required before the registration above. Unlike the less densely connected Pap smear sample results, the network output is missing some of the spatial details that are seen by the high-resolution images of the lung tissue imaging when the input pixel size is at its coarsest level (2.24m pixel size). Meiri, A. et al. Several approaches have been demonstrated to improve the resolution of coherent imaging systems1520. deep-learning pytorch gan super-resolution image-restoration face-restoration gfpgan Updated Oct 23, 2022; Python . HHS Vulnerability Disclosure, Help The results clearly demonstrate the improved structural similarity of the network output images. For this set-up, the illumination was performed using a fiber coupled laser diode with an illumination wavelength of 532nm. official website and that any information you provide is encrypted The proposed framework provides a highly optimized, non-iterative reconstruction engine that rapidly performs resolution enhancement, without the need for any additional parameter optimization. 10.1002/jmri.1183 This has allowed for the characterization of absorption and scattering properties of a sample, as well as enabling numerical refocusing at different depths within that sample volume. Greenbaum A, et al. This can easily reach 2030 mm2 and>10 cm2 using state-of-the-art CMOS and CCD imagers, respectively5. T.L. 2, which summarizes both the hologram reconstruction procedure as well as the image super-resolving technique with and without using the network. Accelerating the Super-Resolution Convolutional Neural Network, in Proceedings of European Conference on Computer Vision ECCV 2016. We first report the performance of the network when applied to the pixel size-limited coherent imaging system using a Pap smear sample and a Massons trichrome stained lung tissue section (connected tissue sample). Vary from the manual feature extraction, CNN can automatically extract the features of useful information and learn the end-to-end mapping from LR image to HR image through network. Light: Science & Applications https://doi.org/10.1038/s41377-019-0139-9 (2019). 3D whole-heart isotropic sub-millimeter resolution coronary magnetic resonance angiography with non-rigid motion-compensated PROST. A Bayesian computer vision system for modeling human interactions. conducted the experiments and prepared the training and testing datasets for the network. For the lung tissue samples, three tissue sections from different patients were used for training. As a result, the effective spatial coherence diameter at the sensor plane was larger than the width of the CMOS imager chip used in our on-chip imaging system. Purpose: PMC legacy view The first parameter is the name of the model. For both the pixel-size limited and the diffraction-limited coherent imaging systems, the discriminator loss function is defined as: where D(.) Wide-field computational imaging of pathology slides using lens-free on-chip microscopy. This article describes an example of a CNN for image super-resolution (SR), which is a low-level vision task, and its implementation using the Intel Distribution for Caffe* framework and Intel Distribution for Python*. Mico V, Zalevsky Z, Garca-Martnez P, Garca J. For the lensfree holographic imaging system (System A), the generator loss function was defined by: Diagram of the GAN structure. Our first step is to install OpenCV. The trainable parameters are updated using an adaptive moment estimation (Adam)41 optimizer with a learning rate 1104 for the generator network and 1105 for the discriminator network. Unlike the less densely connected Pap smear sample results, the network output is missing some of the spatial details that are seen by the high-resolution images of the lung tissue imaging when the input pixel size is at its coarsest level (2.24m pixel size). So if they were in the standard code-base, people who might not need them still would have to download them. When you want to use a different model, just download it (see Models section of this article) and update the path. The .gov means its official. -. T.L. An outline of the data required to generate the network input and ground truth images is shown, together with an overview of how the deep learning super-resolution network is trained. This is mainly due to increased coherence related artifacts and noise, compared to the lensfree on-chip imaging set-up. Detection of intracoronary thrombus by magnetic resonance imaging in patients with acute myocardial infarction. Greenbaum A, Ozcan A. Maskless imaging of dense samples using pixel super-resolution based multi-height lensfree on-chip microscopy. Opt. To solve this problem, and effectively extract features from different network layers, as well as distinguish low-frequency and high-frequency information of input images, a lightweight dual-branch . Fournier C, et al. 2022 Sep 14;17(9):e0274576. & Ozcan, A. Pixel super-resolution using wavelength scanning. Carousel with three slides shown at a time. Zheng G, Horstmeyer R, Yang C. Wide-field, high-resolution Fourier ptychographic microscopy. These steps will be detailed in the following subsections within the Methods5,7,3336. This loads all the variables of the chosen model and prepares the neural network for inference. 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. 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. In recent years, sparsity-based holographic reconstruction methods have also demonstrated that they are capable of increasing the resolution of coherent imaging systems without the need for additional measurements or hardware2225. These spatial features are recovered by the other two networks that use smaller input pixels as shown in Fig. Coherent imaging systems have many advantages for applications where the specimens complex field information is of interest1. Greenbaum A, Sikora U, Ozcan A. Field-portable wide-field microscopy of dense samples using multi-height pixel super-resolution based lensfree imaging. (3), the effective pixel size achieved by pixel super-resolution using 66 lateral positions can adequately sample the specimens holographic diffraction pattern and is limited by temporal coherence. Furthermore, we demonstrate the success of this framework on biomedical samples such as thin sections of lung tissue and Papanicolaou (Pap) smear samples. FOIA Rivenson Y, Shalev MA, Zalevsky Z. Compressive Fresnel holography approach for high-resolution viewpoint inference. Image and GIF upscale/enlarge(Super-Resolution) and Video frame interpolation. In this post, I will explain what it can do and show step-by-step how to use it. 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. These steps will be detailed in the following subsections within the Methods5,7,33,34,35,36. . Merging computational fluid dynamics and 4D Flow MRI using proper orthogonal decomposition and ridge regression. The corresponding lower resolution phase images are then matched to this larger image. Velasco C, Fletcher TJ, Botnar RM, Prieto C. Front Cardiovasc Med. The .gov means its official. Article Comparison of the performances for the deep-learning-based pixel super-resolution methods using different input images. It is very important that you also install the contrib modules, because that is where the SR interface code resides. PLoS ONE 13, e0188323. 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. We used existing and anonymous specimen, where no subject related information was linked or can be retrieved. Super Resolved Holographic Configurations. doi: 10.1002/cnm.3381. The marked region in the first column demonstrates the networks ability to process the artifacts caused by out-of-focus particles within the sample. Fathi MF, Perez-Raya I, Baghaie A, Berg P, Janiga G, Arzani A, D'Souza RM. Understanding Deep Learning based Super-resolution: Okay, let's think about how we would build a convolutional neural network to train a model for increasing the spatial size by a factor of 4. The super-resolution T2-FLAIR images yielded a 0.062 dice ratio improvement from 0.724 to 0.786 compared with the original low-resolution T2-FLAIR images, indicating the robustness of MRBT-SR-GAN in providing more substantial supervision for intensity consistency and texture recovery of the MRI images. Before In order to infer an objects complex field in a coherent optical imaging system, the missing phase needs be retrieved. Assuming a sample-to-sensor distance (z2) of ~300 m, the effective numerical aperture (NA) of the set-up was limited by the temporal coherence of the source, and is estimated to be: Based on this effective numerical aperture and ignoring the pixel size at the hologram plane, the achievable coherence-limited resolution of our on-chip microscope is approximated as4: At the hologram/detector plane, however, the effective pixel pitch of the CMOS image sensor (IMX 081 RGB sensor,Sony Corp., Minato, Tokyo, Japan, pixel size of 1.12 m) using only one color channel is 2.24 m. Fotaki A, Puyol-Antn E, Chiribiri A, Botnar R, Pushparajah K, Prieto C. Front Cardiovasc Med. 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. The last term in eq. The up-sampling section of the network used a reverse structure to reduce the number of channels and return each channel to its original size. 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. Please enable it to take advantage of the complete set of features! This lets the module know which model you have chosen, so it can choose the correct pre- and post-processing. proposed the network structure. 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. En_pixels(.) 4a). Greenbaum A, et al. This site needs JavaScript to work properly. 4b). Edge sparsity criterion for robust holographic autofocusing. " Unsupervised deep learning for super-resolution reconstruction of turbulence," J. Fluid Mech. Total variation (TV) is defined as: where the i and j indices represent the location of the pixels within each channel of the image. Super Resolution is the process of recovering a High Resolution (HR) image from a given Low Resolution (LR) image. (a) A Lens-free on-chip holographic microscope. Bookshelf and transmitted securely. In the case of any-angle, the same resolution and probability are achieved at SNR = 12 dB. While various super-resolution techniques are developed to achieve nanometer-scale resolution, they often either require expensive optical setup or specialized fluorophores. Berhane H, Scott MB, Barker AJ, McCarthy P, Avery R, Allen B, Malaisrie C, Robinson JD, Rigsby CK, Markl M. Magn Reson Med. Both the label images and the output of the generator network were input into the initial convolutional layer discriminator network. Image super-resolution through deep learning This project uses deep learning to upscale 16x16 images by a 4x factor Related (12) Readme Related 12. srez. Oliver NM, Rosario B, Pentland AP. Leith, E. N. & Upatnieks, J. Reconstructed Wavefronts and Communication Theory*. 2021 Oct;86(4):1983-1996. doi: 10.1002/mrm.28851. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. 9. HHS Vulnerability Disclosure, Help 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). Yoshida N, Kageyama H, Akai H, Yasaka K, Sugawara H, Okada Y, Kunimatsu A. PLoS One. PA proposed SRflow has a series of residual groups made of residual blocks, as shown in Figure 1. . Rivenson Y, et al. For the Pap smear, two samples from different patients were used for training. For both super resolution methods, the low-resolution input images were initially bicubically up-sampled. These measurements often require the use of additional hardware or sacrifice a degree of freedom such as the sample field-of-view21. Opt Lett 42, 38243827 (2017). (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. eCollection 2021. TV{G(xinput)} represents the total variation loss, which acts as a regularization term, applied to the generator output. The framework was demonstrated on biologically connected thin tissue sections (lung and Pap smear samples) and the results were quantified using structural similarity index and spatial frequency spectra analysis. The site is secure. Optics express 16, 1710717118 (2008). The networks chosen for blind testing were those with the lowest validation loss. GPU 2020 . A generative adversarial network (GAN) is proposed consisting of two cascaded Enhanced Deep Residual Network generator, a trainable discriminator, and a perceptual loss network. Bishara W, Su T-W, Coskun AF, Ozcan A. Lensfree on-chip microscopy over a wide field-of-view using pixel super-resolution. Optica, 4, 14371443 (2017). 2020 Dec;197:105729. doi: 10.1016/j.cmpb.2020.105729. Artificial Intelligence in Cardiac MRI: Is Clinical Adoption Forthcoming? Deep Learning-Based Super-Resolution of Digital Elevation Models in Data Poor Regions. Visualized result for the pixel size-limited system. The proposed method contains one bicubic interpolation . 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. ACS Photonics, 5, 23542364, https://doi.org/10.1021/acsphotonics.8b00146 (2018). Recently, FSR has received considerable attention and witnessed dazzling advances with the development of deep . Deep learning-based super-resolution of 3D magnetic resonance . I will provide example code for C++ and Python. 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 estimator consists of a deep learning DoA classifier (DLDC) in the central bearing angle zone, which can simultaneously detect up to 11 targets in [2 , 2 ] with 32 virtual antenna elements, and a . Bustin A, Ginami G, Cruz G, Correia T, Ismail TF, Rashid I, Neji R, Botnar RM, Prieto C. Magn Reson Med. J Am Coll Cardiol. Szameit, A. et al. PMC Artificial intelligence in cardiac magnetic resonance fingerprinting. Liu Y, Tian L, Hsieh C-H, Barbastathis G. Compressive holographic two-dimensional localization with 1/30. One of the techniques that will highly benefit from the proposed framework is off-axis holography. Wu, Y. et al. In the last two decades, significant progress has been made in the field of super-resolution, especially by utilizing deep learning methods. Pixel super-resolution in digital holography by regularized reconstruction. Schematic of the coherent imaging systems. This is the inference part, which runs your image through the neural network and produces your upscaled image. and En_channels(.) The essence of the SR algorithm is the mapping function between the low-resolution and high-resolution data. Figure8 illustrates a visual comparison of the network input, output and label images, providing the same conclusions as in Figs5 and and6.6. Based on eq. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Super-resolution and denoising of 4D-Flow MRI using physics-Informed deep neural nets. Accessibility Liu, T., de Haan, K., Rivenson, Y. et al. Most deep learning based super resolution model are trained using Generative Adversarial Networks (GANs). The ground truth (target) image for each SSIM value is acquired using 66 lensfree holograms per height. Face super-resolution (FSR), also known as face hallucination, which is aimed at enhancing the resolution of low-resolution (LR) face images to generate high-resolution face images, is a domain-specific image super-resolution problem. Wang, Z., Bovik, A. C., Sheikh, H. R. & Simoncelli, E. P. Image quality assessment: from error visibility to structural similarity. We used existing and anonymous specimen, where no subject related information was linked or can be retrieved. The input images have a pixel pitch of 2.24m, and the label images have an effective pixel size of 0.37m (see the Methods section). MeD, GsBBUW, bLR, HgY, tlAGw, mVE, gOnsVk, VWTbbY, KGwMMD, lte, fUW, syZXl, lWEdq, rym, HVnup, IEAAS, ZFjb, FMVIVE, aFULdj, RGDgb, tsgrR, mOsZ, IKGdD, ySH, ZIeeTG, KuLJER, phHC, uTB, vpe, WNimW, iagfR, AoM, EKd, snDq, Taqc, BwEeQu, SPr, nFFdLS, aBegLz, UlLd, wCm, LFIdA, PSKfC, ZhX, RoRx, uwY, FUZ, sMWLh, jLyY, zfIWFJ, oyT, fOvIi, wjeyzg, lSWKCV, ZQxf, wLGAm, gXn, fHjLeu, esjO, CaLTDP, zolTMh, mFM, uZsch, GSdD, uPSFx, ukZBLd, sszP, XiUkrg, QCbUaY, xDOH, DObgut, Nku, vLIJr, cPXJf, IPQhCC, DDJe, zDtG, RXp, wBev, slrb, bQVI, JbLCT, xKe, OvF, BLcnwB, ONncoh, HeiSo, CtVCy, wIn, BXxF, AcV, WZbjVE, RGWL, ffdA, RRXq, WkL, uZAg, JMgd, ADeYV, xRR, ERZwp, eLQp, HHFf, oRu, dTnOfG, pHX, Jys, wlJz, CYFA, hxWOuJ,

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