Search Results for author:
Found 22 papers, 7 papers with code
Histopathological Image Analysis with Style-Augmented Feature Domain Mixing for Improved Generalization
Style transfer-based data augmentation is an emerging technique that can be used to improve the generalizability of machine learning models for histopathological images.
Self Supervised Low Dose Computed Tomography Image Denoising Using Invertible Network Exploiting Inter Slice Congruence
We have shown the aforementioned is similar to training a neural network to minimize the distance between clean NDCT and noisy LDCT image pairs.
Sub-Aperture Feature Adaptation in Single Image Super-resolution Model for Light Field Imaging
However, the spatial resolution is significantly constrained in commercial microlens based LF cameras because of the inherent multiplexing of spatial and angular information.
Texture Aware Autoencoder Pre-training And Pairwise Learning Refinement For Improved Iris Recognition
The data relation loss enables learning better texture representation which is pivotal for a texture rich dataset such as iris.
Zero-Shot Single Image Restoration Through Controlled Perturbation of Koschmieder's Model
Our zero-shot network estimates the parameters of the Koschmieder's model, which describes the degradation in the input image, to perform image restoration.
Iterative Gradient Encoding Network with Feature Co-Occurrence Loss for Single Image Reflection Removal
In this study, we proposed an iterative gradient encoding network for single image reflection removal.
Noise Conscious Training of Non Local Neural Network powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising
Next, we moved towards the problem of non-stationarity of CT noise and introduced a new noise aware mean square error loss for LDCT denoising.
Progressive Update Guided Interdependent Networks for Single Image Dehazing
The estimated parameters are then used to guide our dehazing module, where the estimates are progressively updated by novel convolutional networks.
Lightweight Modules for Efficient Deep Learning based Image Restoration
We also present and analyse our results highlighting the drawbacks of applying depthwise separable convolutional kernel (a popular method for efficient classification network) for sub-pixel convolution based upsampling (a popular upsampling strategy for low-level vision applications).
Unsupervised Pre-trained, Texture Aware And Lightweight Model for Deep Learning-Based Iris Recognition Under Limited Annotated Data
In this paper, we present a texture aware lightweight deep learning framework for iris recognition.
SVDocNet: Spatially Variant U-Net for Blind Document Deblurring
Blind document deblurring is a fundamental task in the field of document processing and restoration, having wide enhancement applications in optical character recognition systems, forensics, etc.
The Angel is in the Priors: Improving GAN based Image and Sequence Inpainting with Better Noise and Structural Priors
To our knowledge, this is the first demonstration of an unsupervised GAN based sequence inpainting.
Faster Unsupervised Semantic Inpainting: A GAN Based Approach
In this paper, we propose to improve the inference speed and visual quality of contemporary baseline of Generative Adversarial Networks (GAN) based unsupervised semantic inpainting.
Fast Bayesian Uncertainty Estimation and Reduction of Batch Normalized Single Image Super-Resolution Network
Furthermore, this paper proposes an approach to reduce the model's uncertainty for an input image, and it helps to defend the adversarial attacks on the image super-resolution model.
Improved Techniques for GAN based Facial Inpainting
Current benchmark models are susceptible to initial solutions of non-convex optimization criterion of GAN based inpainting.
Unsupervised Domain Adaptation for Learning Eye Gaze from a Million Synthetic Images: An Adversarial Approach
Such domain difference deteriorates test time performances of models trained on synthetic examples.
Unsupervised Adversarial Visual Level Domain Adaptation for Learning Video Object Detectors from Images
Though image object detectors have shown rapid progress in recent years with the release of multiple large-scale static image datasets, object detection on videos still remains an open problem due to scarcity of annotated video frames.
Retinal Vessel Segmentation under Extreme Low Annotation: A Generative Adversarial Network Approach
The proposed method is an extension of our previous work with the addition of a new unsupervised adversarial loss and a structured prediction based architecture.
Improving Consistency and Correctness of Sequence Inpainting using Semantically Guided Generative Adversarial Network
Contemporary benchmark methods for image inpainting are based on deep generative models and specifically leverage adversarial loss for yielding realistic reconstructions.
Deep Neural Ensemble for Retinal Vessel Segmentation in Fundus Images towards Achieving Label-free Angiography
Automated segmentation of retinal blood vessels in label-free fundus images entails a pivotal role in computed aided diagnosis of ophthalmic pathologies, viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases.
Forward Stagewise Additive Model for Collaborative Multiview Boosting
Also, the proposed model is compared with traditional boosting and recent multiview boosting algorithms.
WEPSAM: Weakly Pre-Learnt Saliency Model
Recent thrust in saliency prediction research is to learn high level semantics using ground truth eye fixation datasets.
