Image Processing Advanced

Deep Learning and Applications

About This Level

The advanced level covers modern image processing centered on deep learning. Starting from image classification with CNNs, it moves through object detection, semantic segmentation, and image generation with GANs and diffusion models, covering many of the core techniques in modern image processing.

Learning Objectives

  • Understand CNN architecture and its application to image classification
  • Master object detection methods such as YOLO, SSD, and Faster R-CNN
  • Learn segmentation methods such as U-Net and DeepLab
  • Understand image generation with GANs, VAEs, and diffusion models
  • Learn specialized applications such as medical image processing

Diagram: CNN Architecture

Input H×W×3 Conv feature maps Pool downsample Conv Pool Flatten FC Output cat: 0.92 dog: 0.05 bird: 0.03 Softmax
Figure 1: CNN architecture — convolution and pooling extract features, and fully connected layers classify. Deeper layers learn more abstract features.

Table of Contents

1. CNN and Image Classification

Foundations of deep learning.

  • Convolution and pooling layers
  • LeNet, AlexNet, VGG
  • ResNet, DenseNet
  • Transfer learning and fine-tuning

2. Object Detection

Joint estimation of location and class.

  • The R-CNN family
  • The YOLO series (v3, v5, v8, v11)
  • SSD, RetinaNet
  • Anchor-free detectors

3. Semantic Segmentation

Pixel-level classification.

  • FCN (Fully Convolutional Network)
  • U-Net and encoder-decoder
  • The DeepLab series
  • Instance segmentation

4. Image Generation Models

Generating new images.

  • VAE (variational autoencoder)
  • GAN and DCGAN
  • StyleGAN, BigGAN
  • Diffusion models (DDPM, Stable Diffusion)

5. Vision Transformer

Applying the attention mechanism.

  • ViT architecture
  • Patch embedding
  • CLIP, DINO
  • Swin Transformer

6. Medical Image Processing

Applications to a specialized field.

  • CT/MRI image preprocessing
  • Organ segmentation
  • Lesion detection
  • 3D image processing

Diagram: Object Detection

Input image YOLO Grid division Joint prediction Fast, end-to-end Detections cat 0.95 dog 0.88 car 0.91
Figure 2: Object detection (YOLO) — the input image is divided into a grid, and each cell predicts bounding boxes, classes, and confidences together in a single forward pass. A fast, end-to-end detector. Each label is drawn as a tab above its box.

Diagram: Image Generation (GAN)

Noise z N(0,1) Generator G Deconv layers image synthesis Generated image G(z) Discriminator D Conv layers real / fake Output Real? 0 or 1 Real image
Figure 3: Image generation (GAN) — the generator G turns noise z into a fake image G(z), and the discriminator D tells G(z) apart from real images. Through this adversarial training, G gradually learns to produce realistic images.

Key Models and Loss Functions

Cross-Entropy Loss (Classification)

$\mathcal{L} = -\displaystyle\sum_{c=1}^{C} y_c \log(\hat{y}_c)$ ($C$: number of classes, $y$: ground truth, $\hat{y}$: prediction)

IoU (Intersection over Union)

An evaluation metric for detection and segmentation: $\text{IoU} = \dfrac{|A \cap B|}{|A \cup B|}$

The GAN Objective

$\min_G \max_D \mathbb{E}_{x}[\log D(x)] + \mathbb{E}_{z}[\log(1 - D(G(z)))]$

Applications You Can Understand at This Level

Autonomous Driving

Environment perception that integrates object detection, segmentation, and depth estimation.

Medical Diagnosis Support

Lesion detection from CT/MRI, organ segmentation, and pathology image analysis.

Image Generation and Editing

Text-to-image, inpainting, style transfer, and super-resolution.

Manufacturing Quality Inspection

Defect detection and automated visual inspection. Reject decisions via anomaly detection.

Supplementary Reading

Readings that build intuition for the essentials of modern image processing and deep learning, using diagrams (including 3D).

Total Variation (TV) Regularization and Denoising

Understand how TV regularization ("data fidelity + an L1 penalty on the gradient") preserves edges.

Compressed Sensing and Sparse Recovery

Understand the principle by which sparsity and L1 minimization recover a signal from few measurements.

Optimal Transport and Images

Understand optimal transport as minimizing the "cost of moving mass" between distributions, giving the Wasserstein distance.

Wavelet Transform and Multiresolution Analysis

Understand wavelets as a multiresolution decomposition local in both position and scale.

Residual Connections and the Loss Landscape

Understand how residual connections ease gradient flow and smooth the loss landscape to aid training.

Transposed Convolution and Upsampling

Understand transposed convolution as "stamping the kernel onto the input" to upsample.

Diffusion Model Noise Schedule

Understand a diffusion model as a "forward process that adds noise" and a "reverse process that predicts and removes it."

Self-Attention, Visualized

Understand self-attention as each token attending to all tokens with learned weights.

Depth Maps and 3D Point Clouds

Understand a depth map as per-pixel distance, convertible into a 3D point cloud by back-projection.

Object Detection Metrics: IoU, NMS, mAP

Understand how IoU, NMS, and mAP handle "match decision," "duplicate removal," and "overall evaluation."

Prerequisites

  • Intermediate-level content (morphology, feature extraction, classical object detection)
  • Foundations of machine learning (loss functions, optimization, overfitting)
  • Basics of Python and PyTorch / TensorFlow
  • Linear algebra, calculus, probability and statistics

Frequently Asked Questions

What does the advanced image processing level cover?

Deep-learning image classification (CNN), object detection (YOLO, SSD, Faster R-CNN), semantic segmentation (U-Net, DeepLab), image generation with GANs and diffusion models, Vision Transformers, and medical image processing. The supplementary readings also cover variational and sparse modeling such as total variation (TV) minimization, compressed sensing, and optimal transport.

What is compressed sensing?

A theory stating that when a signal is sparse (most components are near zero), it can be recovered exactly from far fewer measurements than the usual sampling theorem requires. The solution is found by L1 minimization (a Lasso-type problem), with applications such as fast MRI scanning.

What image processing tasks use total variation (TV) minimization?

Denoising (TV denoising), image restoration, and super-resolution. TV regularization removes noise in flat regions while preserving edges (discontinuities), which makes it important in medical and satellite image processing.