Image Processing With Python

Digital image processing in Python is mostly done via numpy array manipulation.
These videos provide a quick overview of digital images, their data types and numpy array manipulation to modify images.

The videos are created for students and researchers and people interested in image processing using python but with a basic python developer, user in mind.

16 - Understanding digital images for Python processing.
17 - Reading images in Python.
18 - Image processing using pillow in Python.
19 - image processing using scipy in Python.
20 - Introduction to image processing using scikit-image in Python.
21 - Scratch assay analysis with just 5 lines code in Python.
22 - Denoising microscope images in Python.
23 - Histogram based image segmentation in Python.
24 - Random Walker segmentation in Python.
25 - Reading Images, Splitting Channels, Resizing using openCV in Python.
26 - Denoising and edge detection using opencv in Python.
27 - CLAHE and Thresholding using opencv in Python.
28 - Thresholding and morphological operations using openCV in Python.
29 - Key points, detectors and descriptors in openCV.
30 - Image registration using homography in openCV.
32 - Grain size analysis in Python using a microscope image.
33 - Grain size analysis in Python using watershed.
34 - Grain size analysis in Python using watershed - multiple images.
35 - Cell Nuclei analysis in Python using watershed segmentation.
94 - Denoising MRI images (also CT & microscopy images).
95 - What is digital image filtering and image convolution?.
96 - What is Gaussian Denoising Filter?.
97 - What is median denoising filter?.
98 - What is bilateral denoising filter?.
99 - What is Non-local means (NLM) denoising filter?.
100 - What is total variation (TV) denoising filter?.
101 - What is block matching and 3D filtering (BM3D)?.
102 - What is unsharp mask?.
103 - Edge filters for image processing.
104 - Ridge Filters to detect tube like structures in images.
105 - What is Fourier Transform?.
106 - Image filters using discrete Fourier transform (DFT).
112 - Averaging image stack in real and DCT space for denoising.
113 - Histogram equalization and CLAHE.
114 - Automatic image quality assessment using BRISQUE.
115 - Auto segmentation using multi-otsu.
Effect of Social Distancing on the spread of COVID-19 pandemic - A quick Python simulation.
107 - Analysis of COVID-19 data using Python - Part 1.
108 - Analysis of COVID-19 data using Python - Part 2.
109 - Predicting COVID-19 cases using Python.
110 - Visualizing COVID-19 cases & death information using Python and plotly.
111 - What are the top 10 countries with highest COVID-19 cases and deaths?.
116 - Measuring properties of labeled / segmented regions.
117 - Shading correction using rolling ball background subtraction.
118 - Object detection by template matching.
119 - Sub-pixel image registration in Python.
123 - Reference based image quality metrics.
124 - Image quality by estimating sharpness.
146 - Raspberry Pi - Learning python and deep learning on a tight budget.
182 - How to batch process multiple images in python?.
183 - OCR in python using keras-ocr.
191 - Measuring image similarity in python.
192 - Working with 3D and multi-dimensional images in python.
199 - Detecting straight lines using Hough transform in python.
200 - Image classification using gray-level co-occurrence matrix (GLCM) features and LGBM classifier.
201 - Working with geotiff files using rasterio in python (also quick demo of NDVI calculation).
202 - Two ways to read HAM10000 dataset into python for skin cancer lesion classification.
203 - Skin cancer lesion classification using the HAM10000 dataset.
204 - U-Net for semantic segmentation of mitochondria.
205 - U-Net plus watershed for instance segmentation.
206 - The right way to segment large images by applying a trained U-Net model on smaller patches.
207 - Using IoU (Jaccard) as loss function to train U-Net for semantic segmentation.
208 - Multiclass semantic segmentation using U-Net.
209 - Multiclass semantic segmentation using U-Net: Large images and 3D volumes (slice by slice).
210 - Multiclass U-Net using VGG, ResNet, and Inception as backbones.
69 - Image classification using Bag of Visual Words (BOVW).
211 - U-Net vs LinkNet for multiclass semantic segmentation.
212 - Classification of mnist sign language alphabets using deep learning.
213 - Ensemble of networks for improved accuracy in deep learning.
214 - Improving semantic segmentation (U-Net) performance via ensemble of multiple trained networks.
215 - 3D U-Net for semantic segmentation.
216 - Semantic segmentation using a small dataset for training (& U-Net).
218 - Difference between UpSampling2D and Conv2DTranspose used in U-Net and GAN.
219 - Understanding U-Net architecture and building it from scratch.
220 - What is the best loss function for semantic segmentation?.
221 - Easy way to split data on your disk into train, test, and validation?.
222 - Working with large data that doesn't fit your system memory - Semantic Segmentation.
223 - Test time augmentation for semantic segmentation.
224 - Recurrent and Residual U-net.
225 - Attention U-net. What is attention and why is it needed for U-Net?.
226 - U-Net vs Attention U-Net vs Attention Residual U-Net - should you care?.
227 - Various U-Net models using keras unet collection library - for semantic image segmentation.
228 - Semantic segmentation of aerial (satellite) imagery using U-net.
229 - Smooth blending of patches for semantic segmentation of large images (using U-Net).
230 - Semantic Segmentation of Landcover Dataset using U-Net.
231 - Semantic Segmentation of BraTS2020 - Part 0 - Introduction (and plan).