Description
Over the last decade, smartphone cameras have improved significantly, becoming the primary device people use for capturing everyday moments and high-quality photographs. This progress is largely due to advances in computational photography and novel image sensors. Computational photography enables great images from compact mobile cameras, enhancing photos through various techniques such as multi-shot merging. Despite these advancements, challenges such as noise, artifacts, and distortions persist, especially in low-light conditions where limited light increases noise levels. In this lightning talk, we will explore how PyTorch can be used to design and optimize deep learning networks for real-time low-light denoising. We will dive into noise modeling, data generation, physics-aware models, and advanced network architectures for effective denoising in challenging low-light scenarios. Attendees will gain practical insights into the latest advancements in mobile computational photography using PyTorch.