🌅The code of post "Image retrieval using MatconvNet and pre-trained imageNet"

FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising (TIP, 2018)

Learning a Single Convolutional Super-Resolution Network for Multiple Degradations (CVPR, 2018) (Matlab)

Efficient, transparent deep learning in hundreds of lines of code.

CNN Image Retrieval in MatConvNet: Training and evaluating CNNs for Image Retrieval in MatConvNet

WB color augmenter improves the accuracy of image classification and image semantic segmentation methods by emulating different WB effects (ICCV 2019) [Python & Matlab].

Depth Map Super-Resolution by Deep Multi-Scale Guidance, ECCV 2016

Large-scale, Fast and Accurate Shot Boundary Detection through Spatio-temporal Convolutional Neural Networks

Q. Zhang, Q. Yuan, C. Zeng, X. Li, and Y. Wei, “Missing Data Reconstruction in Remote Sensing image with a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Network,” IEEE TGRS, 2018.

Source code for the 2019 IEEE TIFS paper "PalmNet: Gabor-PCA Convolutional Networks for Touchless Palmprint Recognition"

Implementation code of the paper "Deep Fully Convolutional Regression Networks for Single Image Haze Removal"

Semantic information can help CNNs to get better illuminant estimation -- a proof of concept

Learning-based Just-noticeable-quantization-distortion Model for perceptual video coding

CNN-Based Resource Allocation for Energy-Efficient D2D Communications

3-Class Retinal Classification via Deep Network Features and SVM Classifier (Academic Research Use)

Controllable List-wise Ranking for Universal No-reference Image Quality Assessment

Discriminative Unsupervised Feature Learning with Convolutional Neural Networks

A neural network toolbox for matlab

This repository includes the source code of the CNN-based channel estimators proposed in "CNN Aided Weighted Interpolation for Channel Estimation in Vehicular Communications" paper [1] that is published in the IEEE Transactions on Vehicular Technology, 2021.

[BMVC17] Weakly Supervised Saliency Detection with A Category-Driven Map Generator