Reinforcement learning from human feedback

Enhancing image quality involves refining models through reinforcement learning from human feedback, utilizing cost-effective evaluation data from photo experts. Unlike the current method of manually creating supervised learning datasets for training enhancement models, which is both labor-intensive and costly, this process can be limiting in terms of data collection.

Reinforcement learning from human feedback (RLHF) offers a promising, more budget-friendly alternative to expanding dataset sizes. It enables the continuous enhancement of models without the need for continually amassing larger training datasets with diminishing returns

Multi-exposure imaging enhanced by AI

Multi-exposure imaging enhanced by AI represents a groundbreaking fusion of advanced photography techniques with cutting-edge artificial intelligence. In this process, our AI algorithms analyze multiple exposures of the same scene, intelligently blending them to create a final image with superior dynamic range and detail retention.

Through sophisticated machine learning models, our AI identifies and combines the optimal elements from each exposure, ensuring that highlights are not overexposed and shadows are not lost in darkness. This results in visually stunning photographs that exhibit enhanced clarity, depth, and realism, surpassing what traditional photography methods can achieve alone. By harnessing the power of AI, photographers can effortlessly capture scenes with unparalleled richness and fidelity, even in challenging lighting conditions.

The project named Image enhancement with single/multiple exposures (AIERE) strives for automation of color & brightness image transformation with deep neural networks.