Search Results for author:
Found 11 papers, 8 papers with code
Spatial Coherence Loss for Salient and Camouflaged Object Detection and Beyond
We also demonstrate that combining SCLoss with other loss functions can further improve performance and result in the SOTA outcomes for different applications.
Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model
These priors are subsequently utilized by RGformer to guide the decomposition of image features into their respective reflectance and illumination components.
Directional Connectivity-based Segmentation of Medical Images
To this end, we propose a directional connectivity modeling scheme for segmentation that decouples, tracks, and utilizes the directional information across the network.
RetiFluidNet: A Self-Adaptive and Multi-Attention Deep Convolutional Network for Retinal OCT Fluid Segmentation
Experimental results on the datasets prove the effectiveness of the proposed model in retinal OCT fluid segmentation and reveal that the suggested method is more effective than existing state-of-the-art fluid segmentation algorithms in adapting to retinal OCT scans recorded by various image scanning instruments.
BiconNet: An Edge-preserved Connectivity-based Approach for Salient Object Detection
Salient object detection (SOD) is viewed as a pixel-wise saliency modeling task by traditional deep learning-based methods.
Modeling Extremes with d-max-decreasing Neural Networks
We propose a novel neural network architecture that enables non-parametric calibration and generation of multivariate extreme value distributions (MEVs).
Mesoscopic photogrammetry with an unstabilized phone camera
We present a feature-free photogrammetric technique that enables quantitative 3D mesoscopic (mm-scale height variation) imaging with tens-of-micron accuracy from sequences of images acquired by a smartphone at close range (several cm) under freehand motion without additional hardware.
Identifying Latent Stochastic Differential Equations
We present a method for learning latent stochastic differential equations (SDEs) from high-dimensional time series data.
Fisher Auto-Encoders
It has been conjectured that the Fisher divergence is more robust to model uncertainty than the conventional Kullback-Leibler (KL) divergence.
Learning Partial Differential Equations from Data Using Neural Networks
We develop a framework for estimating unknown partial differential equations from noisy data, using a deep learning approach.
MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints
Image post-processing is used in clinical-grade ultrasound scanners to improve image quality (e. g., reduce speckle noise and enhance contrast).
