Clearing Up the Picture

Current vision algorithms are largely designed for use in clear conditions, deep learning using convoluted neural networks is now being harnessed to improve visual performance in adverse weather. Capturing high-quality photos is easier than ever, as filters and image-adjustment tools can enhance images. Yet cameras still struggle to provide a clear image in bad weather, especially in extreme conditions such as heavy rain, fog, or poor lighting at night. Objects in a scene can become hard to see or even invisible, especially when they are far from the lens, and colors are often dulled. "In rain and snow, you also have motion blur because they are moving," says Dengxin Dai, a computer vision lecturer at ETH Zurich in Switzerland who coordinated a workshop on all-weather vision at the Conference for Computer Vision and Pattern Recognition (CVPR 2019) in Long Beach, CA. "So the geometry of an object might also get distorted."