Overview
Parallel image processing is a powerful technique that leverages multiple processors to enhance the speed and efficiency of image processing tasks. By dividing tasks among processors, it allows for real-time applications and the handling of large datasets, which is increasingly important in fields l...
Key Terms
Example: Concurrency allows multiple image processing tasks to be executed at the same time.
Example: Image segmentation is used in medical imaging to isolate tumors.
Example: Data distribution is essential for efficient parallel processing.
Example: Common performance metrics include processing time and resource utilization.
Example: OpenMP can be used to parallelize loops in image processing applications.
Example: CUDA allows developers to use GPUs for parallel image processing.