Reduces unnecessary image-processing workload by focusing analysis on relevant image patches while learning recurring noise patterns, potentially improving processing efficiency and scalability.

Extracting meaningful identification data from video or image frames can be challenging when captured areas contain noise, incomplete information or irrelevant image patches, potentially affecting the accuracy and efficiency of image analysis.

Provides an image-processing system that divides video frames into predefined patches, identifies patches containing relevant identification data, extracts the information and continuously learns and stores patches classified as noise to improve subsequent processing.

Introduces patch-level image processing that identifies relevant information while simultaneously learning and storing noise patches, allowing the system to improve its ability to distinguish useful image data from recurring noise.