CDAN: Convolutional Dense Attention-guided Network for Low-
Upload an image and do basic manipulation tasks via OpenCV
Enhance image brightness and apply various adjustments
Brighten low light images
ehnace your own photos based on user inputs
Edit image brightness and apply various filters
Image Augmentation is a technique used in computer visio.
Try out image augmentation techniques.
Relight images with custom prompts
Enhance and edit images with brightness, sharpness, and contrast adjustments
Enhance low-light images using a predefined model
Apply filters to enhance your photos
Enhance low light photos to make them clearer
CDAN (Convolutional Dense Attention-guided Network for Low-light image enhancement) is an AI-powered tool designed to enhance low-light images. It leverages advanced convolutional neural networks and attention mechanisms to improve visibility and clarity in dimly lit photos. CDAN is specifically tailored to address the challenges of low-light photography, making it a valuable tool for photographers and casual users alike.
• AI-guided enhancement: Uses deep learning to intelligently adjust brightness, contrast, and color balance.
• Real-time processing: Quickly transforms low-light images with minimal latency.
• Dynamic range adjustment: Balances overexposed and underexposed areas seamlessly.
• Noise reduction: Minimizes grain and digital noise common in low-light conditions.
• Detail preservation: Maintains sharpness and fine details in the enhanced image.
What types of images does CDAN work best with?
CDAN is optimized for low-light images, especially those with poor visibility or excessive noise. It works best with photos taken in dimly lit environments.
Can I adjust settings manually?
Yes, CDAN allows manual adjustments for brightness, contrast, and color balance, giving you control over the final output.
Is CDAN suitable for professional photography?
Absolutely! CDAN is designed to deliver professional-grade results, making it a great tool for photographers needing to enhance low-light shots.