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Unsharp masking image enhancement the parallel algorithm based on cross-platform.

Yupu Song1, Cailin Li2,3, Shiyang Xiao4

  • 1College of Computer Engineering, Shangqiu Polytechnic, Shangqiu, 476000, Henan, China.

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|November 24, 2022
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Summary
This summary is machine-generated.

This study introduces a parallel unsharp masking algorithm using Open Computing Language (OpenCL) to enhance image processing efficiency. The OpenCL algorithm significantly reduces execution time for high-resolution image enhancement compared to traditional methods.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Parallel Computing

Background:

  • Traditional unsharp masking algorithms face limitations in computational efficiency and platform scalability.
  • Existing methods struggle with processing large datasets of high-resolution images in real-time.

Purpose of the Study:

  • To develop a parallel unsharp masking image enhancement algorithm leveraging Open Computing Language (OpenCL).
  • To improve computational efficiency and overcome platform limitations of existing unsharp masking techniques.

Main Methods:

  • Analyzed parallel characteristics of the unsharp masking algorithm for parallel implementation.
  • Optimized global and constant memory allocation based on data access patterns and OpenCL storage models.
  • Utilized fast local memory access modes and logical data space transformation to enhance on-chip memory bandwidth utilization.

Main Results:

  • The OpenCL-based parallel algorithm achieved a maximum speedup of 16.71 times compared to the CPU serial algorithm.
  • Maintained equivalent image quality while significantly reducing algorithm execution time.
  • Demonstrated high performance and platform portability across different hardware.

Conclusions:

  • The proposed OpenCL accelerated unsharp masking algorithm offers a significant improvement in processing speed for image enhancement.
  • Provides an effective reference for real-time processing of large, high-resolution image datasets.
  • Highlights the potential of parallel computing with OpenCL for demanding image processing tasks.