A naukri.com initiative
PlanetPython
1M
414
Image Credit: PlanetPython
Python GUIs: Build an Image Noise Reduction Tool with Streamlit and OpenCV ��� Clean up noisy images using OpenCV
- Image noise is a random variation of brightness or color in images, making it harder to discern finer details in a photo.
- Noise reduction improves the visual appearance of photos, crucial in image analysis pipelines.
- Multiple algorithms are available for denoising images, and the tutorial focuses on using OpenCV algorithms within a Streamlit app.
- Users can upload images, choose noise reduction algorithms (e.g., Gaussian Blur, Median Blur), adjust strength, and download the processed image.
- The tutorial covers setting up the working environment, building the app outline, uploading images, and understanding how Streamlit works.
- It demonstrates converting uploaded images for processing, configuring noise reduction algorithms using sliders, and applying algorithms like Gaussian Blur and Median Blur.
- The tutorial discusses noise reduction methods like Minimum Blur and Maximum Blur for specific noise types, along with Non-Local Means Denoising.
- Improvements include adjusting layout for better user experience and enabling image download in different formats (PNG, JPEG).
- Code structure is enhanced by segregating UI and processing logic into separate functions for better readability and maintenance.
- In conclusion, the tutorial showcases building an interactive noise reduction app using Streamlit and OpenCV, offering a user-friendly way to enhance images.
Read Full Article
24 Likes
For uninterrupted reading, download the app