Search Results for author:
Found 14 papers, 3 papers with code
Turb-Seg-Res: A Segment-then-Restore Pipeline for Dynamic Videos with Atmospheric Turbulence
Tackling image degradation due to atmospheric turbulence, particularly in dynamic environment, remains a challenge for long-range imaging systems.
Unsupervised Microscopy Video Denoising
In this paper, we introduce a novel unsupervised network to denoise microscopy videos featured by image sequences captured by a fixed location microscopy camera.
Unsupervised Region-Growing Network for Object Segmentation in Atmospheric Turbulence
In this paper, we present a two-stage unsupervised foreground object segmentation network tailored for dynamic scenes affected by atmospheric turbulence.
Unsupervised Coordinate-Based Video Denoising
In this paper, we introduce a novel unsupervised video denoising deep learning approach that can help to mitigate data scarcity issues and shows robustness against different noise patterns, enhancing its broad applicability.
Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists
A novel deep learning-based method was utilized for assessment of knee OA in two steps: (1) localization of knee joints in the images, (2) classification according to the KL grading system.
NIMBLE: A Non-rigid Hand Model with Bones and Muscles
Emerging Metaverse applications demand reliable, accurate, and photorealistic reproductions of human hands to perform sophisticated operations as if in the physical world.
Unsupervised Non-Rigid Image Distortion Removal via Grid Deformation
In this paper, we present a novel unsupervised network to recover the latent distortion-free image.
Learning To Remove Refractive Distortions From Underwater Images
The fluctuation of the water surface causes refractive distortions that severely downgrade the image of an underwater scene.
Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model
While breast cancer screening has been one of the most studied medical imaging applications of artificial intelligence, the development and evaluation of the algorithms are hindered due to the lack of well-annotated large-scale publicly available datasets.
Deep Surface Light Fields
A surface light field represents the radiance of rays originating from any points on the surface in any directions.
Personalized Saliency and its Prediction
Specifically, we propose to decompose a personalized saliency map (referred to as PSM) into a universal saliency map (referred to as USM) predictable by existing saliency detection models and a new discrepancy map across users that characterizes personalized saliency.
Rotational Crossed-Slit Light Field
In this paper, we present a novel LF sampling scheme by exploiting a special non-centric camera called the crossed-slit or XSlit camera.
A Weighted Sparse Coding Framework for Saliency Detection
In this paper, we present a unified saliency detection framework for handling heterogenous types of input data.
Saliency Detection on Light Field
Existing saliency detection approaches use images as inputs and are sensitive to foreground/background similarities, complex background textures, and occlusions.
