Image Augmentation is a technique used in computer visio.
low light image enhance
Replace background in product images
Upload an image and do basic manipulation tasks via OpenCV
Enhance low light photos to make them clearer
Transform images based on textual instructions
Generate images with customized lighting effects
Customize and adapt dark and light themes on a web interface
Brighten low light images
Enhance your photos with AI-driven adjustments
Enhance low-light images using a predefined model
Adjust web theme to light, dark, or OLED
CDAN: Convolutional Dense Attention-guided Network for Low-
Image Augmentation is a technique used in computer vision to artificially increase the size of a training dataset by applying transformations to existing images. It helps improve model performance by exposing the model to a wider variety of scenarios, reducing overfitting, and enhancing generalization. With features like flip, rotate, brightness adjustment, and crop, this technique is essential for training robust machine learning models.
• Change Lighting: Adjust brightness, contrast, and saturation to simulate different lighting conditions. • Geometric Transformations: Rotate, flip, and crop images to create diverse perspectives. • Noise Addition: Add random noise to images for robustness against real-world variations. • Color Jittering: Modify color balance to mimic real-life camera inconsistencies. • Scaling: Resize images to test model performance at different resolutions.
What is Image Augmentation used for?
Image Augmentation is used to enhance dataset diversity, improving model generalization and reducing overfitting in machine learning tasks.
Can Image Augmentation be applied to videos?
Yes, Image Augmentation techniques can be applied to video frames, enabling similar benefits for video-based machine learning models.
What file formats does Image Augmentation support?
Image Augmentation typically supports common formats like JPEG, PNG, and BMP, depending on the specific implementation.