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This project involves performing a valid convolution on a 300x300 image using a 5x5 kernel (stride 1) with multithreading. The goal is to efficiently apply the convolution filter using multiple threads and display the results on a histogram graph. The implementation ensures thread safety and utilizes OpenMP for parallelization.
This is an academic experiment comparing CPU and GPU performance using CUDA and OpenMP. It involves implementing three algorithms: Standard Deviation Calculation, Image Convolution, and Histogram-Based Data Structure, optimised for parallel execution to demonstrate performance improvements on different hardware architectures.
Image processing in Python. Reading, converting to different formats, implementing filtering, convolving images, detecting edges, cropping and resizing images