image processing concepts

Techniques

Results

References

MATLAB used to turn the rape leaf image into a grayscale image

Their technique based on OpenMP.

Their optimization approach significantly improves the performance and the maximum speedup achieved can reach 1.59.

Tang et al. [3]

Serial and Parallel Technologies with Image Processing

Using Inverse Filter

Their technique makes use of OpenMP and OpenCV.

They achieved a speed-up and a reduction in processing time by using a parallel computing approach to bone scan image processing compared to a serial computing approach.

Mallegowda, M et al. [4]

Image Mean Filtering Based on OpenCL

Their technique involves a hierarchical weighted mean filtering parallel algorithm specifically designed for OpenCL, which leverages multi-layer GPU architecture to efficiently distribute image processing tasks across workgroups and work-items for accelerated performance.

By parallelizing their image processing algorithms on GPUs with OpenCL, the researchers achieved efficient and high-speed processing of large datasets, preparing their work for real-time applications in the face of ever-growing data volumes.

Han Xiao et al. [5]

Multicore image processing using OpenMP

Their technique used OpenMP

By leveraging parallelism through OpenMP, programmers can modify their single-threaded code to run efficiently on multiple cores, thereby potentially enhancing the performance of image processing algorithms.

Greg Slabaugh et al. [6]