Enhance Low Light Images Using Keras, Python and Weights & Biases
Learn to enhance low light images using Zero-DCE (Zero-Reference Deep Curve Estimation) in this 36-minute video tutorial. Explore an unsupervised learning approach that requires only dark, low light images to produce impressive results. Discover how to implement this technique using Python, Keras, and Weights & Biases, resulting in a compact 350 KB model capable of real-time image enhancement. Engage with Soumik Rakshit, the creator of the Keras implementation, as he discusses the strengths and weaknesses of Zero-DCE. Gain hands-on experience in tracking machine learning experiments with Weights & Biases and Keras, and learn to use W&B Tables for exploring model predictions. Follow along with a provided Google Colab notebook to train your own Zero-DCE model and visualize results on test datasets.