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Found 15 papers, 9 papers with code
PMS-Net: Robust Haze Removal Based on Patch Map for Single Images
Conventional patch-based haze removal algorithms (e. g. the Dark Channel prior) usually performs dehazing with a fixed patch size.
Assessing Graph-based Deep Learning Models for Predicting Flash Point
To further explore GBDL models, we collected the largest flash point dataset to date, which contains 10575 unique molecules.
PMHLD: Patch Map Based Hybrid Learning DehazeNet for Single Image Haze Removal
In addition, to further enhance the performance of the method for haze removal, a patch-map-based DCP has been embedded into the network, and this module has been trained with the atmospheric light generator, patch map selection module, and refined module simultaneously.
ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-Tree Complex Wavelet Representation and Contradict Channel Loss
Moreover, due to the limitation of existing snow datasets, to simulate the snow scenarios comprehensively, we propose a large-scale dataset called Comprehensive Snow Dataset (CSD).
CREATe: Clinical Report Extraction and Annotation Technology
Clinical case reports are written descriptions of the unique aspects of a particular clinical case, playing an essential role in sharing clinical experiences about atypical disease phenotypes and new therapies.
Multi-modal Bifurcated Network for Depth Guided Image Relighting
This model extracts the image and the depth features by the bifurcated network in the encoder.
S3Net: A Single Stream Structure for Depth Guided Image Relighting
Depth guided any-to-any image relighting aims to generate a relit image from the original image and corresponding depth maps to match the illumination setting of the given guided image and its depth map.
LAFFNet: A Lightweight Adaptive Feature Fusion Network for Underwater Image Enhancement
The model is the encoder-decoder model with multiple adaptive feature fusion (AAF) modules.
ContourletNet: A Generalized Rain Removal Architecture Using Multi-Direction Hierarchical Representation
The rainy scenarios can be categorized into two classes: moderate rain and heavy rain scenes.
Learning Multiple Adverse Weather Removal via Two-Stage Knowledge Learning and Multi-Contrastive Regularization: Toward a Unified Model
At the KC, the student network aims to learn the comprehensive bad weather removal problem from multiple well-trained teacher networks where each of them is specialized in a specific bad weather removal problem.
RVSL: Robust Vehicle Similarity Learning in Real Hazy Scenes Based on Semi-supervised Learning
Recently, vehicle similarity learning, also called re-identification (ReID), has attracted significant attention in computer vision.
DehazeNeRF: Multiple Image Haze Removal and 3D Shape Reconstruction using Neural Radiance Fields
Neural radiance fields (NeRFs) have demonstrated state-of-the-art performance for 3D computer vision tasks, including novel view synthesis and 3D shape reconstruction.
Counting Crowds in Bad Weather
Crowd counting has recently attracted significant attention in the field of computer vision due to its wide applications to image understanding.
JSTASR: Joint Size and Transparency-Aware Snow Removal Algorithm Based on Modified Partial Convolution and Veiling Effect Removal
It can classify snow particles according to their sizes and conduct snow removal in different scales.
