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).