Topical Collection on “Traditional and Machine Learning Methods to Solve Imaging Problems”

Imaging problems, including image reconstruction, restoration, enhancement, segmentation,classification, object detection, and secure image transmission, have traditionallybeen addressed using analytical methods derived from mathematical models and domainspecificknowledge. Regularization, filtering, optimization, geometric modeling, andstatistical estimation remain important because their assumptions and behavior can oftenbe understood and controlled explicitly. More recently, Machine Learning (ML) and DeepLearning (DL) methods have achieved impressive results by learning complex image representationsdirectly from large datasets. Current research increasingly seeks to combinethese two perspectives. Instead of treating analytical and learning-based methods as competingalternatives, hybrid approaches incorporate physical constraints, signal-processingprinciples, or application-specific knowledge into trainable systems. This can improve dataefficiency, interpretability, robustness, and computational performance while retaining therepresentational power of modern neural networks. [...]