Classical Denoising Methods

Here we present an extensive overview of threshold-based denoising methodologies. Beyond classical approaches like compromise thresholding (which balances hard and soft thresholding using a weighted parameter) and adaptive thresholding (where thresholds dynamically adjust based on local image characteristics), numerous alternative methods are available. For instance, gradient-based thresholding techniques utilize image gradient magnitudes to determine optimal thresholds, while local variance methods compute thresholds based on statistical variations within pixel neighborhoods. Modern implementations also incorporate deep learning architectures, such as convolutional neural networks (CNNs) with automated feature extraction, to significantly enhance denoising performance. These methods can be selectively implemented using key functions like cv2.adaptiveThreshold() for adaptive approaches or custom CNN architectures for deep learning solutions, allowing optimal denoising results tailored to specific data types and application requirements.