{"id":18498523,"url":"https://github.com/yashkathe/image-noise-reduction-with-cuda","last_synced_at":"2025-05-14T05:23:40.610Z","repository":{"id":213557730,"uuid":"723143953","full_name":"yashkathe/Image-Noise-Reduction-with-CUDA","owner":"yashkathe","description":"This project conducts an analysis of image denoising technique - median blur, comparing GPU-accelerated (Numba) and CPU-based (OpenCV) processing speeds.","archived":false,"fork":false,"pushed_at":"2024-03-17T21:14:19.000Z","size":26047,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2024-12-25T17:42:57.247Z","etag":null,"topics":["cuda","cuda-programming","gpu-programming","hardware-speed-analysis","image-analysis","image-processing","numba","nvidia","nvidia-cuda","nvidia-gpu","opencv","parallel-programming"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/yashkathe.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2023-11-24T20:00:32.000Z","updated_at":"2024-12-22T20:16:42.000Z","dependencies_parsed_at":"2024-03-17T22:38:01.022Z","dependency_job_id":null,"html_url":"https://github.com/yashkathe/Image-Noise-Reduction-with-CUDA","commit_stats":{"total_commits":4,"total_committers":1,"mean_commits":4.0,"dds":0.0,"last_synced_commit":"5d0f21ada86e28040e358c5b9f16277e19db1e7e"},"previous_names":["yashkathe/cs217-image-noise-reduction-with-cuda","yashkathe/image-noise-reduction-with-cuda"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yashkathe%2FImage-Noise-Reduction-with-CUDA","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yashkathe%2FImage-Noise-Reduction-with-CUDA/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yashkathe%2FImage-Noise-Reduction-with-CUDA/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yashkathe%2FImage-Noise-Reduction-with-CUDA/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/yashkathe","download_url":"https://codeload.github.com/yashkathe/Image-Noise-Reduction-with-CUDA/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239217107,"owners_count":19601593,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cuda","cuda-programming","gpu-programming","hardware-speed-analysis","image-analysis","image-processing","numba","nvidia","nvidia-cuda","nvidia-gpu","opencv","parallel-programming"],"created_at":"2024-11-06T13:41:00.661Z","updated_at":"2025-05-14T05:23:40.603Z","avatar_url":"https://github.com/yashkathe.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# GPU vs CPU Runtime Analysis\n\n![alt text](output_image/image_1.png)\n\n## Overview\n\nThis repository contains a project that compares the performance of image\nprocessing operations when executed on a GPU vs. a CPU. The focus is on\nanalyzing the execution time for median filtering across a set of images,\nproviding insights into the efficiency gains achievable with GPU acceleration.\n\n## Project Description\n\nThe project uses Python, with OpenCV for CPU-based image processing and Numba\nfor GPU acceleration. The primary goal is to measure and compare the execution\ntime for median filtering—an image denoising technique—on both the CPU and GPU.\n\n## Key Features\n\n- Image Processing: Applies median filtering to a set of images using both CPU and GPU.\n- Runtime Comparison: Measures and logs the execution time for both methods.\n- Visualization: Displays original and processed images side by side for visual comparison.\n\n## Getting Started\n\n### Prerequisites\n\n- Python 3.x\n- Libraries: OpenCV, Numba, Matplotlib, NumPy\n- CUDA-enabled GPU for running GPU-accelerated code\n\n### Installation\n\nClone this repository or download the source code.  \nInstall required Python packages:\n\n```bash\npip install opencv-python numba matplotlib numpy\n```\n\n### Usage\n\nPlace your images in the image-data directory.  \nRun the Jupyter Notebook.  \nThe notebook will process the images and display the results along with the\nruntimes.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyashkathe%2Fimage-noise-reduction-with-cuda","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fyashkathe%2Fimage-noise-reduction-with-cuda","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyashkathe%2Fimage-noise-reduction-with-cuda/lists"}