Learning A Deep Convolution Network with Turing Test Adversaries for Microscopy Image Super Resolution

  title={Learning A Deep Convolution Network with Turing Test Adversaries for Microscopy Image Super Resolution},
  author={Francis Tom and Himanshu Sharma and Dheeraj Mundhra and Tathagato Rai Dastidar and Debdoot Sheet},
  journal={2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)},
Adversarially trained deep neural networks have significantly improved performance of single image super resolution, by hallucinating photorealistic local textures, thereby greatly reducing the perception difference between a real high resolution image and its super resolved (SR) counterpart. However, application to medical imaging requires preservation of diagnostically relevant features while refraining from introducing any diagnostically confusing artifacts. We propose using a deep… Expand
Super-resolution of satellite imagery using a wavelet multiscale-based deep convolutional neural network model
A satellite image SISR algorithm is designed and implemented by estimating high frequency details through training Deep Convolutional Neural Network (DCNNs) with respect to wavelet analysis to improve the spatial resolution of multispectral remote sensing images captured by DubaiSat-2 satellite. Expand
Zero-Shot Adaptation to Simulate 3D Ultrasound Volume by Learning a Multilinear Separable 2D Convolutional Neural Network
A multilinear separable 2D convolutional neural network using 1D convolutions to model PSF family along direction of ultrasound propagation and orthogonal to it is adversarially trained using a visual Turing test on 2D ultrasound images. Expand


Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
  • C. Ledig, Lucas Theis, +6 authors W. Shi
  • Computer Science, Mathematics
  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2017
SRGAN, a generative adversarial network (GAN) for image super-resolution (SR), is presented, to its knowledge, the first framework capable of inferring photo-realistic natural images for 4x upscaling factors and a perceptual loss function which consists of an adversarial loss and a content loss. Expand
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
This work thoroughly study three key components of SRGAN – network architecture, adversarial loss and perceptual loss, and improves each of them to derive an Enhanced SRGAN (ESRGAN), which achieves consistently better visual quality with more realistic and natural textures than SRGAN. Expand
Deep Learning Microscopy
It is demonstrated that a deep neural network can significantly improve optical microscopy, enhancing its spatial resolution over a large field-of-view and depth of field, and can be used to design computational imagers that get better and better as they continue to image specimen and establish new transformations among different modes of imaging. Expand
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
This work considers image transformation problems, and proposes the use of perceptual loss functions for training feed-forward networks for image transformation tasks, and shows results on image style transfer, where aFeed-forward network is trained to solve the optimization problem proposed by Gatys et al. in real-time. Expand
Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images
A Spatially Constrained Convolutional Neural Network (SC-CNN) to perform nucleus detection and a novel Neighboring Ensemble Predictor (NEP) coupled with CNN to more accurately predict the class label of detected cell nuclei are proposed. Expand
Deep learning enhanced mobile-phone microscopy
The use of deep learning is reported on to correct distortions introduced by mobile-phone-based microscopes, facilitating the production of high-resolution, denoised and colour-corrected images, matching the performance of benchtop microscopes with high-end objective lenses, also extending their limited depth-of-field. Expand
The Perception-Distortion Tradeoff
It is proved mathematically that distortion and perceptual quality are at odds with each other, and generative-adversarial-nets (GANs) provide a principled way to approach the perception-distortion bound. Expand
Very Deep Convolutional Networks for Large-Scale Image Recognition
This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. Expand
Super-resolution image reconstruction: a technical overview
The goal of this article is to introduce the concept of SR algorithms to readers who are unfamiliar with this area and to provide a review for experts to present the technical review of various existing SR methodologies which are often employed. Expand
The relativistic discriminator: a key element missing from standard GAN
It is shown that RGANs and RaGANs are significantly more stable and generate higher quality data samples than their non-relativistic counterparts, and Standard RaGAN with gradient penalty generate data of better quality than WGAN-GP while only requiring a single discriminator update per generator update. Expand