Image Processing: Enhancement and Analysis

Related lectures (31)

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Covers practical image processing using Fiji software, emphasizing data quality importance.

Explores turbulence characteristics, simulation methods, and modeling challenges, providing guidelines for choosing and validating turbulence models.

Covers the setup of a Gitlab agent for Kubernetes, focusing on installation, version control, and troubleshooting.

Explores noise reduction techniques in electrical metrology, covering properties of charge, current, voltage, noise sources, and filtering methods.

Covers Gaussian Mixture Models, Denoising, Data Classification, and Spike Sorting using Principal Component Analysis.

Explores noise in electronics, covering average power, signal-to-noise ratio, deterministic and random signals, and noise amplification.

Explores optical image sensors, their history, architecture, specifications, and noise suppression techniques.

Explores weight sharing, filters, channels, and data augmentation in convolutional neural networks.

Explores noise reduction techniques in electrical systems, covering concepts like Fourier transform, impedance matching, and dithering.

Explores neighborhood averaging, Gaussian smoothing, median filtering, contrast enhancement, and edge detection in image processing.