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Found 11 papers, 4 papers with code
I2V: Towards Texture-Aware Self-Supervised Blind Denoising using Self-Residual Learning for Real-World Images
Although the advances of self-supervised blind denoising are significantly superior to conventional approaches without clean supervision in synthetic noise scenarios, it shows poor quality in real-world images due to spatially correlated noise corruption.
MitoVis: A Visually-guided Interactive Intelligent System for Neuronal Mitochondria Analysis
It is known that the morphology of mitochondria is closely related to the functions of neurons and neurodegenerative diseases.
ColorRL: Reinforced Coloring for End-to-End Instance Segmentation
Instance segmentation, the task of identifying and separating each individual object of interest in the image, is one of the actively studied research topics in computer vision.
RLCorrector: Reinforced Proofreading for Cell-level Microscopy Image Segmentation
Segmentation of nanoscale electron microscopy (EM) images is crucial but still challenging in connectomics research.
ISCL: Interdependent Self-Cooperative Learning for Unpaired Image Denoising
With the advent of advances in self-supervised learning, paired clean-noisy data are no longer required in deep learning-based image denoising.
Noise2Kernel: Adaptive Self-Supervised Blind Denoising using a Dilated Convolutional Kernel Architecture
In this paper, we propose a dilated convolutional network that satisfies an invariant property, allowing efficient kernel-based training without random masking.
Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistency
Segmentation is a fundamental process in microscopic cell image analysis.
Reinforced Coloring for End-to-End Instance Segmentation
To exploit the advantages of conventional single-object-per-step segmentation methods without impairing the scalability, we propose a novel iterative deep reinforcement learning agent that learns how to differentiate multiple objects in parallel.
Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Loss
In this paper, we propose a novel deep learning-based generative adversarial model, RefineGAN, for fast and accurate CS-MRI reconstruction.
ssEMnet: Serial-section Electron Microscopy Image Registration using a Spatial Transformer Network with Learned Features
The alignment of serial-section electron microscopy (ssEM) images is critical for efforts in neuroscience that seek to reconstruct neuronal circuits.
FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics
Electron microscopic connectomics is an ambitious research direction with the goal of studying comprehensive brain connectivity maps by using high-throughput, nano-scale microscopy.
