Single Image Dehazing Edit Task
Computer Vision • Dehazing
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GIMP-ML: Python Plugins for using Computer Vision Models in GIMP
Apart from these, several image manipulation techniques using these plugins have been compiled and demonstrated in the YouTube channel (https://youtube. com/user/kritiksoman) with the objective of demonstrating the use-cases for machine learning based image modification.
COLORIZATION DEBLURRING DENOISING IMAGE INPAINTING IMAGE MANIPULATION IMAGE SUPER-RESOLUTION MONOCULAR DEPTH ESTIMATION SEMANTIC SEGMENTATION SINGLE IMAGE DEHAZING VIDEO FRAME INTERPOLATION
Densely Connected Pyramid Dehazing Network
CVPR 2018
• hezhangsprinter/DCPDN
• 
We propose a new end-to-end single image dehazing method, called Densely Connected Pyramid Dehazing Network (DCPDN), which can jointly learn the transmission map, atmospheric light and dehazing all together.
FFA-Net: Feature Fusion Attention Network for Single Image Dehazing
The FFA-Net architecture consists of three key components: 1) A novel Feature Attention (FA) module combines Channel Attention with Pixel Attention mechanism, considering that different channel-wise features contain totally different weighted information and haze distribution is uneven on the different image pixels.
Ranked #1 on
Image Dehazing
on SOTS Indoor
GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing
ICCV 2019
• proteus1991/GridDehazeNet
•
The proposed hazing method does not rely on the atmosphere scattering model, and we provide an explanation as to why it is not necessarily beneficial to take advantage of the dimension reduction offered by the atmosphere scattering model for image dehazing, even if only the dehazing results on synthetic images are concerned.
DIMENSIONALITY REDUCTION IMAGE DEHAZING SINGLE IMAGE DEHAZING
Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing
In this paper, we present an end-to-end network, called Cycle-Dehaze, for single image dehazing problem, which does not require pairs of hazy and corresponding ground truth images for training.
Ranked #3 on
Image Dehazing
on O-Haze
Improved Techniques for Learning to Dehaze and Beyond: A Collective Study
Here we explore two related but important tasks based on the recently released REalistic Single Image DEhazing (RESIDE) benchmark dataset: (i) single image dehazing as a low-level image restoration problem; and (ii) high-level visual understanding (e. g., object detection) of hazy images.
IMAGE DEHAZING IMAGE RESTORATION OBJECT DETECTION SINGLE IMAGE DEHAZING
Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset
The attenuation depends both on the water body and the 3D structure of the scene, making color restoration difficult.
Feature Forwarding for Efficient Single Image Dehazing
Haze degrades content and obscures information of images, which can negatively impact vision-based decision-making in real-time systems.
Ranked #2 on
Image Dehazing
on O-Haze
DECISION MAKING IMAGE CROPPING IMAGE DEHAZING SINGLE IMAGE DEHAZING SUPER-RESOLUTION
Dense Haze: A benchmark for image dehazing with dense-haze and haze-free images
Characterized by dense and homogeneous hazy scenes, Dense-Haze contains 33 pairs of real hazy and corresponding haze-free images of various outdoor scenes.
Progressive Feature Fusion Network for Realistic Image Dehazing
• MKFMIKU/PFFNet
•
Most of them follow a classic atmospheric scattering model which is an elegant simplified physical model based on the assumption of single-scattering and homogeneous atmospheric medium.