Image processing techniques for image analysis

Image analysis involves processing an image into fundamental components to extract meaningful information. Image analysis can include tasks such as finding shapes, detecting edges, removing noise, counting objects, and calculating statistics for texture analysis or image quality.

Image analysis is a broad term that covers a range of techniques that generally fit into these subcategories:

Examples of image analysis techniques include:

Enhancing low-light images through haze removal.

Segmenting an image using Gabor filters and k-means clustering.

Removing Gaussian noise with a pre-trained neural network.

Extracting statistical data (left) and filtering images based on region properties (right) using the Image Region Analyzer app.

For more information on image analysis with MATLAB®, see Image Processing Toolbox™.

See also: color profile, image thresholding, image enhancement, image reconstruction, image segmentation, image transform, image registration, digital image processing, image processing and computer vision, Steve on Image Processing, affine transformation, lab color, deep learning, point cloud, 3D image processing