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DEEP LEARNING METHODS FOR IMAGE PROCESSING WORKFLOWS
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Deep learning has fundamentally transformed image processing from hand-crafted algorithmic pipelines into end-to-end learned systems capable of human-surpassing performance across classification, detection, segmentation, generation, and restoration tasks. Convolutional neural networks replace decades-old filtering, thresholding, and feature engineering with hierarchical feature extractors learning directly from raw pixels through millions of parameterized filters trained via gradient descent. This paradigm shift eliminates brittle cascade architectures where edge detection failure propagates through Hough voting to tracking collapse, replacing sequential failure modes with robust holistic understanding emerging from statistical training. Modern vision transformers extend convolutional foundations through global self-attention mechanisms modeling long-range spatial dependencies absent from purely local receptive fields.
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