Since Gabors seminal work, various optical and numerical techniques have been suggested2 to acquire the complex field of a coherently illuminated specimen. Researching AI and computer vision. One of the limitations of GANs is that they are effectively a lazy approach as their loss function, the critic, is trained as part of the process and not specifically engineered for this purpose. Wide-field computational imaging of pathology slides using lens-free on-chip microscopy. 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). This allows the imaging system to have a large imaging field-of-view (FOV) that is only limited by the active area of the opto-electronic image sensor chip. De-identified Hematoxylin and Eosin (H&E) stained human lung tissue slides were acquired from the UCLA Translational Pathology Core Laboratory. Disclaimer, National Library of Medicine To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The other authors do not have competing interests. The last term in eq. Five-minute whole-heart coronary MRA with sub-millimeter isotropic resolution, 100% respiratory scan efficiency, and 3D-PROST reconstruction. For the pixel size-limited coherent imaging system (System A), the super-resolved images were created by collecting multiple low-resolution holograms at different lateral positions, where the CMOS image sensor was sub-pixel shifted by a mechanical stage (MAX606, Thorlabs Inc., Newton, NJ, USA)to create a shift table. The desktop uses an Nvidia GTX 1080 Ti GPU, a Core i7-7900K CPU running at 3.3GHz, and 64 GB of RAM. For the diffraction-limited coherent imaging system (System B), an additional rough FOV matching step was required before the registration above. 4. 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. https://doi.org/10.1038/s41598-019-40554-1, DOI: https://doi.org/10.1038/s41598-019-40554-1. The authors also acknowledge the Translational Pathology Core Laboratory (TPCL) and the Histology Lab at UCLA for their assistance with the sample preparation. The interface or module we will use is called dnn_superres (dnn stands for Deep Neural Network; superres for Super Resolution). . and En_channels(.) Provided by the Springer Nature SharedIt content-sharing initiative, International Journal of Computer Assisted Radiology and Surgery (2022). 2012;60:2316-2322. Effect of windowing and zero-filled reconstruction of MRI data on spatial resolution and acquisition strategy. They can all upscale images by a scale of 2, 3 and 4. This is mainly due to increased coherence related artifacts and noise, compared to the lensfree on-chip imaging set-up. To obtain JMRI 14, 270280. Similar to the down sampling section, each block contained two convolutional layers, each activated by a LReLU layer. 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. 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. Med Phys. Non-Rigid Respiratory Motion Estimation of Whole-Heart Coronary MR Images Using Unsupervised Deep Learning. Phase recovery and holographic image reconstruction using deep learning in neural networks. En_pixels(.) End-to-end deep learning nonrigid motion-corrected reconstruction for highly accelerated free-breathing coronary MRA. Things you should do as a Data Scientist, other than Data Science. Careers. Would you like email updates of new search results? The sample is a Massons trichrome stained lung tissue slide, imaged at an illumination wavelength of 550nm. 8600 Rockville Pike Spatial frequency analysis for the pixel size-limited system. & Pentland, A. P. A Bayesian computer vision system for modeling human interactions. The input images were obtained using a 4/0.13 NA objective lens and the reference ground truth images were obtained by using a 10/0.30 NA objective lens. These data-driven super-resolution approaches produce a trained deep convolutional neural network that learns to transform low-resolution images into high-resolution images in a single feed-forward (i.e., non-iterative) step. Google Scholar. The resulting 64x64 images display sharp features that are plausible based on the dataset that was used to train the neural net. Second, we present some important works on remote sensing image super-resolution, such as training and . Scientific Reports (Sci Rep) Epub 2019 Aug 7. 6 This can lead to the loss of detail in bright regions, however, the majority of the extended features have X-ray counts below 200 . The marked region in the first column demonstrates the networks ability to process the artifacts caused by out-of-focus particles within the sample. (CDGANs) for RS images, which is a GAN-based super-resolution algorithm, to solve the 'discrimination-ambiguity . are the expectation values for the pixels with in each image and the channels of each image, respectively. Deep learning-based approaches for super-resolution of incoherent microscopy modalities such as brightfield and fluorescence microscopy have also recently emerged2630. They only show a very small increase from a value of 0.876 for the input image to 0.879 for the network output. When upscaling, you can assign the upscaled image directly, instead of creating a placeholder image: There are currently 4 different SR models supported in the module. Therefore, the achievable resolution is limited by the temporal coherence length of the illumination37, which is defined as: where n=1 is the refractive index. The first parameter is the name of the model. Goodman, J. W. Statistical optics (John Wiley & Sons, 2015). Publishers note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. The first convolution was used to maintain the size of the output, and the second doubled the number of channels while halving the size of the output in each lateral dimension. Sinha, A., Lee, J., Li, S. & Barbastathis, G. Lensless computational imaging through deep learning. The FOV of each tissue image was ~20 mm2 (corresponding to the sensor active area). Nature Methods 16, 103110 (2019). JACC Cardiovasc Imaging. Manuscript related data can be requested from the corresponding author. Try different models to get different results in terms of speed and performance. and transmitted securely. You can choose between: edsr, fsrcnn, lapsrn, espcn. This registration process correlated the spatial patterns of the phase images and used the correlation to establish an affine transform matrix. and JavaScript. Deep learning (DL) has been extensively used to 'supersample' the pixels in computationally downsampled digital photographs 1, 2, 3, 4. Based on eq. Figure8 illustrates a visual comparison of the network input, output and label images, providing the same conclusions as in Figs5 and 6. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Opt. Article Federal government websites often end in .gov or .mil. 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. & Ozcan, A. Maskless imaging of dense samples using pixel super-resolution based multi-height lensfree on-chip microscopy. For lung tissue sections, we proved the efficacy of our super-resolution technique (Fig. Abstract: In recent years, deep learning has made great progress in many fields such as image recognition, natural language processing, speech recognition and video super-resolution. This big difference can make training a deep learning model very unstable and therefore, we clip pixel values to 200 times the mean background rate B = 1.1168 10 5 counts s 1 for the denoising data set and 50 B for the (2x) super resolution data set. These measurements often require the use of additional hardware or sacrifice a degree of freedom such as the sample field-of-view21. 2011;124:416-424. Fluids 33, 013601 (2021). This algorithm requires accurate knowledge of the sample-to-sensor distances used. 2022 Sep 20;9:1009131. doi: 10.3389/fcvm.2022.1009131. 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. Furthermore, we demonstrate the success of this framework on biomedical samples such as thin sections of lung tissue and Papanicolaou (Pap) smear samples. Nature 161, 777778 (1948). The super-resolution method based on deep learning has been validated in natural images. We need to download the pre-trained models separately, because the OpenCV code-base does not contain them. Comparison of the performances for the deep-learning-based pixel super-resolution methods using different input images. 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. The other authors do not have competing interests. 9. Velasco C, Fletcher TJ, Botnar RM, Prieto C. Front Cardiovasc Med. The interface contains pre-trained models that can be used for inference very easily and efficiently. Accessibility Wu, Y. et al. K.D. Deep Laplacian Pyramid Super-Resolution Network (LapSRN), the current strategy, is based on the CNN SR model. Super-Resolution 846 papers with code 4 benchmarks 25 datasets Super resolution is the task of taking an input of a low resolution (LR) and upscaling it to that of a high resolution. Epub 2020 Dec 29. 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. 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. supervised the project. Similar to the pixel size-limited coherent imaging system, we analyzed the performance of our network using spatial frequency analysis, the results of which are reported in Fig. 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). Deep learning-based super-resolution of 3D magnetic resonance . Lets look at C++ first: I will explain the important parts of the code. The .gov means its official. is the founder of a company that commercializes computational imaging technologies. The second parameter is the upscaling factor, i.e. Our generator network used an adapted U-net architecture40. Synthetic aperture superresolution with multiple off-axis holograms. Edge sparsity criterion for robust holographic autofocusing. Applied optics 56, 6977 (2017). . [1] Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee, Enhanced Deep Residual Networks for Single Image Super-Resolution, 2nd NTIRE: New Trends in Image Restoration and Enhancement workshop and challenge on image super-resolution in conjunction with CVPR 2017. The https:// ensures that you are connecting to the Deep learning microscopy. 2021 Jan;40(1):444-454. doi: 10.1109/TMI.2020.3029205. Image registration plays a key role in generating the training and testing image pairs for the network in both the pixel size-limited and diffraction-limited coherent imaging systems. For the pixel-super-resolution network (System A), the network training process is demonstrated in Fig. The proposed technique might be used to bridge the space-bandwidth-product gap between off-axis and in-line coherent imaging systems, while retaining the single-shot and high sensitivity advantages of off-axis image acquisition systems. 2016 ), efficient sub-pixel convolutional neural network (espcn) (shi et al. Figure7 reports the 2-D spatial frequency spectra and the associated radially-averaged frequency intensity of the network input, network output and the ground truth images corresponding to our lensfree on-chip imaging system. The resolution in this case is limited by the NA of the objective lens. Careers. I will provide example code for C++ and Python. Detection of intracoronary thrombus by magnetic resonance imaging in patients with acute myocardial infarction. Bishara W, Su T-W, Coskun AF, Ozcan A. Lensfree on-chip microscopy over a wide field-of-view using pixel super-resolution. This can easily reach 2030 mm2 and>10 cm2 using state-of-the-art CMOS and CCD imagers, respectively5. For this set-up, the illumination was performed using a fiber coupled laser diode with an illumination wavelength of 532nm. We have presented a GAN-based framework that can super-resolve images taken using both pixel size limited and diffraction limited coherent imaging systems. Using the same GAN-based approach, we also improved the resolution of a lens-based holographic imaging system that was limited in resolution by the numerical aperture of its objective lens. FOIA HHS Vulnerability Disclosure, Help deep-learning pytorch gan super-resolution image-restoration face-restoration gfpgan Updated Oct 23, 2022; Python . This PhD project aims to develop a deep learning based method to improve the resolution of images and videos based on public dataset and experimental dataset. sharing sensitive information, make sure youre on a federal Schematic of the training process for deep-learning based pixel super-resolution. In conclusion, we showed that a deep learning-based approach has great potential when it comes to increasing the resolution of low-field MR images. (b) Structure of the discriminator portion of the network. 2021 The Authors. To perform this, an initial zero-phase was assigned to the intensity/amplitude measurement at the 1st hologram height. 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. The networks chosen for blind testing were those with the lowest validation loss. & Ozcan, A. Field-portable wide-field microscopy of dense samples using multi-height pixel super-resolution based lensfree imaging. 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. A.O. Deep learning enables cross-modality super-resolution in fluorescence microscopy. First, we briefly summarize the methods that we have used in this paper; sub-sequent subsections will provide more information on specific methods employed in our work. SSIM values are also shown for the network input and output images for each case. Image super-resolution (SR) is one of the vital image processing methods that improve the resolution of an image in the field of computer vision. J. Biomech. Hemodynamics in a giant intracranial aneurysm characterized by in vitro 4D flow MRI. The proposed technique might be used to bridge the space-bandwidth-product gap between off-axis and in-line coherent imaging systems, while retaining the single-shot and high sensitivity advantages of off-axis image acquisition systems. proposed the network structure. De-identified Hematoxylin and Eosin (H&E) stained human lung tissue slides were acquired from the UCLA Translational Pathology Core Laboratory. 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. For the down-sampling section, these residual blocks consisted of two convolution layers with LReLU units acting upon them. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 831843 (2000). Spatial light interference microscopy (SLIM). We applied the presented deep learning-based super-resolution approach to two separate in-line holographic imaging systems to demonstrate the efficacy of the technique. Wu, Y. et al. For the down-sampling section, these residual blocks consisted of two convolution layers with LReLU units acting upon them. yt-dlp is a youtube-dl fork based on the now . For both types of coherent imaging systems, holograms at 8 different sample-to-sensor distances were collected to perform the multi-height phase recovery5,7,33,34,35,36. Epub 2020 Sep 15. Would you like email updates of new search results? 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. Teague MR. Deterministic phase retrieval: a Greens function solution. All the networks were trained with a batch size of 10 using 128128 pixel patches. Training-free, single-image super-resolution using a dynamic convolutional network. is the founder of a company that commercializes computational imaging technologies. For this set-up, the illumination was performed using a fiber coupled laser diode with an illumination wavelength of 532nm.
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