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multiscale-super-spectral-1

Accurate Spectral Super-resolution from Single RGB Image Using Multi-scale CNN

multispec

MultiSpec is a freeware image data analysis application developed for interactively analyzing Earth observational multispectral and hyperspectral image data.

multispectral-image-analysis-tool

2020 Winter Co-op project at Sungkyunkwan University

multispectral_images

Multiespectral Images

multiview_dataset_mnist

music4p3

MUltiband Satellite Imagery for object Classification (MUSIC) to detect Photovoltaic Power Plants

mvet

Resource and code for multiview entity typing

named-entity-recognition-bidirectionallstm-cnn-conll

Keras implementation of "Few-shot Learning for Named Entity Recognition in Medical Text"

namedentityrecognition

ner_final

neural-classifiers-with-few-audio

Training neural audio classifiers with few data − https://arxiv.org/abs/1810.10274

neuralstat

Reproduction study of neural statistician

nkfd

MATLAB Code for Kernel linear discriminant analysis with Noisy Labels.

node-image-classification

A simple example on Image Classification in Node.js with TensorFlow.js

noise-filtering-and-image-registration

In this project I implemented a program that can take a rotated image and map it to a non rotated selfie image. The program uses the concept of Image Registration, mapping coordinates of an image based on landmarks to a function such that the function maps each pixel in the rotated image to an interpolated version of the image where the face is no longer rotated. In addition to this, I also take a satellite image of Saturn taken by NASA in 2005 which depicted periodic and salt and pepper noise. I then accurately filter out all noise from the image using my understanding of median filters and frequency domain analysis. I filter out periodic noise using a high pass notch filter to remove the periodic noise in the frequency domain. To execute the function run the main.m script and select the landmarks for both images. In the supplied code, the landmarks have been hardcoded in with the values used in the report, to test your own landmark values uncomment the 3 lines of “cpselect” commands in the main.m file, and comment out the hardcoded values for p, p1, p2, q, q1, q2. Further details can be found in the pdf report.

noise-removal

Sample Python code for Mean & Median filters and sample convolution implementation.

noise2noise

An unofficial and partial Keras implementation of "Noise2Noise: Learning Image Restoration without Clean Data"

noise2noise_pytorch

PyTorch Implementation of Noise2Noise

noisereduce

Noise reduction in python using spectral gating (speech, bioacoustics, audio, time-domain signals)

noiseremoval-image-processing

Removing noise by using Min, Max, Gaussian and other types of filters using MATLAB.

noisy-labeling

A visual simulation of ML model performance when the labeling function is noisy.

noisy-labels-problem-collection

This is a collection of Papers and Codes for Noisy Labels Problem.

noisy-oracle

Replicate the paper "Efficiently Learning the Accuracy of Labeling Sources for Selective Sampling" and explore the ensemble method

noisy_label

Code for the CVPR15 paper "Learning from Massive Noisy Labeled Data for Image Classification"

noisy_label-1

noisy_labels

TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER

noisylabel

noisylabeleddataanalysis

noisylabelexperiments

noisylabels

Experiments with noisy labels, to se the accuracy of classification under noise, effects of active learning, etc