Tanvir-jemin

CUHKSZ_DIP

GAN-DataAugmentation

The project shows implementation based on a variety of applications of convolutional networks. DCGAN structure is used for model construction on GAN to learn deeply with multiple convolutional layers. The accuracy for classification task is about doubled after being supported by GAN-based data augmentation. This quantity is even significantly increased with the suitable selection of batch size, which is distinctly differentiated with 2 given label categories. Particularly, by being provided by more high-quality synthesized images, CNN for cow can obtain up to 95% accuracy with the adequate batch size for the training process on GAN. Lastly, the application of surface-feature extraction of PatchGAN trained along with CycleGAN considering the cycle consistency loss of image reconstruction from a distribution to the target one helps grasping the mapping between them and creates generators able to synthesize the transferred version containing the style feature of the objective distribution from the image of the original one without requiring any paired supportive similarity.

MFIF-PADCDTNP

MiT-MFIF

mfif-laplacian-pyramid

Multi-focus image fusion based on Laplacian pyramid.

Multi-Focus-Image-Fusion

MFIF with Light Field data

DRL-FPD-MFIF

MFIF-GAN

This is an implementation for our paper "MFIF-GAN: A New Generative Adversarial Network for Multi-Focus Image Fusion".

SF_MFIF

multi-focus image fusion

FusionNet

Deep Multifocus Image Fusion Net

Registration-AutoFuse

AutoFuse: Automatic Fusion Networks for Unsupervised and Semi-supervised Medical Image Registration

medical-image-fusion

this is repository about medical image fusion.

MSDNet-for-Medical-Image-Fusion

ICIG2019

DecompositionForFusion

Code for "Fusion from Decomposition: A Self-Supervised Decomposition Approach for Image Fusion"(ECCV2022)

SFGAN

Semantic Fusion GAN for semi-supervised image classification

edcstfn

An Enhanced Deep Convolutional Model for Spatiotemporal Image Fusion

HiFuse

HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image Classification

ResNetFusion

The code of "Infrared and visible image fusion via detail preserving adversarial learning"

PMGI_AAAI2020

Code of paper Rethinking the Image Fusion: A Fast Unified Image Fusion Network based on Proportional Maintenance of Gradient and Intensity

FusionDN

Code of FusionDN (AAAI 2020): A Unified Densely Connected Network for Image Fusion

road-scene-infrared-visible-images

Datasets: road-scene-infrared-visible-images, for feature matching, image registration, and image fusion

Zero-Learning-Fast-Medical-Image-Fusion

A real-time image fusion method using pre-trained neural networks

multimodal-image-fusion-to-detect-brain-tumors

Multi-modal medical image fusion to detect brain tumors using MRI and CT images

Generation-of-SAR-Images-Using-Deep-Learning

Generation of Synthetic Aperture Radar (SAR) images using Generative Adversarial Networks (GANs)

DCGAN-tensorflow

A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"

Face-generation-GAN

Generate realistic human images that do not exist in reality using GAN

Image_Enhancement

imagefusion_densefuse

DenseFuse (IEEE TIP 2019, Highly Cited Paper) - Python 3.6, TensorFlow 1.8.0

ESPCN

A PyTorch implementation of ESPCN based on CVPR 2016 paper "Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network"

Image-Super-Resolution

Implementation of Super Resolution CNN in Keras.