An Overview of Image Processing Techniques and Common Tools for Beginners

This article is aimed at readers interested in image technology who have not received formal training, offering a brief overview of the field, clarifying its main directions, and introducing commonly used toolkits to give beginners a holistic understanding.

Image technology can be divided into three hierarchical layers: the most basic layer consists of university‑level mathematics, the middle layer covers general image processing techniques, and the top layer focuses on specific application research. In most companies, image development is product‑oriented, while only large enterprises, universities, or research institutes conduct foundational research.

The article does not present mathematical formulas; it concentrates on general image processing concepts, definitions, and research topics, helping readers choose an entry point based on their interests. The three fundamental math subjects are applicable across all higher‑level directions, though specialized topics such as stereoscopic geometry or convex optimization are beyond the scope of this piece.

Image processing applies signal‑processing techniques to the image domain, involving analysis, manipulation, and enhancement of images. It overlaps with image analysis and typically includes image coding/decoding, transforms, enhancement and restoration, edge detection, spatial and frequency filtering, morphological operations, and segmentation. The following figure illustrates how different filtering kernels affect image quality.

Commonly used libraries for image processing include OpenCV, VLFeat, MATLAB, Intel IPP, PIL, and Halcon. In industry, OpenCV dominates due to its comprehensive functionality, speed, effectiveness, and extensive documentation. A recommended introductory textbook is Gonzalez’s “Digital Image Processing”.

Image classification separates images into categories based on feature information and is a fundamental computer‑vision task. Traditional approaches rely on handcrafted features and separate classifiers, whereas deep‑learning methods learn high‑discriminative features automatically, allowing simple classifiers to be integrated within a single model.

Since the advent of deep learning, classification accuracy has surpassed human performance in many scenarios, and large‑scale competitions such as CIFAR‑10/100, ImageNet, and WebVision have driven rapid progress. ImageNet, organized by Fei‑Fei Li, remains the most influential benchmark with 15 million images and over 20 thousand categories.

Traditional techniques often follow a Bag‑of‑Words pipeline: low‑level features (e.g., HOG, histograms) are extracted, spatially encoded, transformed for robustness, and finally fed to classifiers such as K‑Nearest Neighbors or SVM.

Deep‑learning approaches use convolutional neural networks (CNNs) for feature extraction and simple classifiers for prediction, trained on massive labeled datasets via supervised or semi‑supervised learning. Popular frameworks include Caffe, TensorFlow, PyTorch, MXNet, and PaddlePaddle.

Object detection aims to locate and size specific targets within an image, facing challenges such as varying appearance, pose, occlusion, and illumination. Deep‑learning models now achieve impressive detection results, as illustrated in the figure below.

Before deep learning, HOG and Deformable Part Models (DPM) were the state‑of‑the‑art detectors, with DPM winning multiple VOC challenges. After deep learning, methods like OverFeat and R‑CNN accelerated progress, leading to dominant one‑stage detectors (YOLO, SSD) and two‑stage detectors (Faster R‑CNN).

Semantic segmentation performs pixel‑level classification, assigning all pixels belonging to the same object class to a single label, offering greater completeness than traditional segmentation. Example results are shown below.

Current algorithms such as DeepLab and Mask‑RCNN still face edge artifacts like jaggedness and merging, limiting their applicability in fine‑grained scenarios.

Geometric transformation utilizes image geometry for spatial manipulation, including calibration, 3‑D reconstruction, and stereo vision, with broad applications in 3D mapping, VR/AR, and film production.

Cameras often produce barrel or pincushion distortion that must be calibrated; industrial systems typically address this, while consumer cameras embed correction. A current hot application is VISLAM, which achieves localization and navigation using optical cameras.

In summary, the five major image‑processing domains—basic processing, classification, detection, segmentation, and geometric transformation—form the foundation for most research and product development. The rise of deep learning has dramatically lowered the entry barrier, enabling newcomers to directly learn classification and detection techniques for practical use.