sZoom: A Framework for Automatic Zoom into High Resolution Surveillance Videos

Saini, Mukesh; Guthier, Benjamin; Kuang, Hao; Mahapatra, Dwarikanath; Saddik, Abdulmotaleb El

doi:10.48550/arxiv.1909.10164

Cited by 5 publications

(5 citation statements)

References 29 publications

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“…In previous work [88,89,90,91,92,93] we used generative adversarial networks (GANs) for multimodal retinal image registration where the trained network outputs the registered image and the deformation field. We show that by incorporating appropriate constraints in the adversarial loss and content loss function, generative models are fairly reliable in directly generating the registered image and its corresponding deformation field.…”

Section: Related Workmentioning

confidence: 99%

Generative Adversarial Networks And Domain Adaptation For Training Data Independent Image Registration

Mahapatra¹

2019

Preprint

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Medical image registration is an important task in automated analysis of multimodal images and temporal data involving multiple patient visits. Conventional approaches, although useful for different image types, are time consuming. Of late, deep learning (DL) based image registration methods have been proposed that outperform traditional methods in terms of accuracy and time. However, DL based methods are heavily dependent on training data and do not generalize well when presented with images of different scanners or anatomies. We present a DL based approach that can perform medical image registration of one image type despite being trained with images of a different type. This is achieved by unsupervised domain adaptation in the registration process and allows for easier application to different datasets without extensive retraining.To achieve our objective we train a network that transforms the given input image pair to a latent feature space vector using autoencoders. The resultant encoded feature space is used to generate the registered images with the help of generative adversarial networks (GANs). This feature transformation ensures greater invariance to the input image type. Experiments on chest Xray, retinal and brain MR images show that our method, trained on one dataset gives better registration performance for other datasets, outperforming conventional methods that do not incorporate domain adaptation.

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Section: Related Workmentioning

confidence: 99%

Generative Adversarial Networks And Domain Adaptation For Training Data Independent Image Registration

Mahapatra¹

2019

Preprint

Self Cite

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“…Also, CNN methods capture mostly local context information and do not explore the global aspects. Zhang et al in [191,153,152,155,157,24], [22,130,151], [79,51,78,49], [42,43,11], [90,77,88], [168,8,75,76] propose a squeeze and excitation network to capture the global characteristics thus leading to improved super resolution output. However, squeeze and excitation relies on CNN features to capture global context which is not optimal.…”

Section: Introductionmentioning

confidence: 99%

Improved Super Resolution of MR Images Using CNNs and Vision Transformers

Mahapatra¹

2022

Preprint

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State of the art magnetic resonance (MR) image super-resolution methods (ISR) using convolutional neural networks (CNNs) leverage limited contextual information due to the limited spatial coverage of CNNs. Vision transformers (ViT) learn better global context that is helpful in generating superior quality HR images. We combine local information of CNNs and global information from ViTs for image super resolution and output super resolved images that have superior quality than those produced by state of the art methods. We include extra constraints through multiple novel loss functions that preserve structure and texture information from the low resolution to high resolution images.

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“…Traditional augmentations such as image rotations or deformations have limited benefit as they do not fully represent the underlying data distribution of the training set and are sensitive to parameter choices. Recent data augmentation methods of [25], [131], [11], [113], [108], [106], [114], [129], [103], [69], [139], [39], [32], [33], [13], [73], [62], [63] use generative adversarial network (GAN), [ [23]], and show moderate success for medical image classification. However, they have limited relevance for segmentation since they do not model geometric relation between different organs and most augmentation approaches do not differentiate between normal and diseased samples.…”

Section: Introductionmentioning

confidence: 99%

CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis

Mahapatra¹,

Singh²

2021

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While medical image segmentation is an important task for computer aided diagnosis, the high expertise requirement for pixelwise manual annotations makes it a challenging and time consuming task. Since conventional data augmentations do not fully represent the underlying distribution of the training set, the trained models have varying performance when tested on images captured from different sources. Most prior work on image synthesis for data augmentation ignore the interleaved geometric relationship between different anatomical labels. We propose improvements over previous GAN-based medical image synthesis methods by learning the relationship between different anatomical labels. We use a weakly supervised segmentation method to obtain pixel level semantic label map of images which is used learn the intrinsic relationship of geometry and shape across semantic labels. Latent space variable sampling results in diverse generated images from a base image and improves robustness. We use the synthetic images from our method to train networks for segmenting COVID-19 infected areas from lung CT images. The proposed method outperforms state-of-theart segmentation methods on a public dataset. Ablation studies also demonstrate benefits of integrating geometry and diversity.

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sZoom: A Framework for Automatic Zoom into High Resolution Surveillance Videos

Cited by 5 publications

References 29 publications

Generative Adversarial Networks And Domain Adaptation For Training Data Independent Image Registration

Generative Adversarial Networks And Domain Adaptation For Training Data Independent Image Registration

Improved Super Resolution of MR Images Using CNNs and Vision Transformers

CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis

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