Image Restoration using Online Photo Collections

We present an image restoration method that leverages a large database of images gathered from the web. Given an input image, we execute an efficient visual search to find the closest images in the database; these images define the input’s visual context. We use the visual context as an image-specific prior and show its value in a variety of image restoration operations, including white balance correction, exposure correction, and contrast enhancement. We evaluate our approach using a database of 1 million images downloaded from Flickr and demonstrate the effect of database size on performance. Our results show that priors based on the visual context consistently out-perform generic or even domain-specific priors for these operations.

• Type: Conference
• Year: 2009
• Where: ICCV
• Authors: Kevin Dale, Micah K. Johnson, Kalyan Sunkavalli, Wojciech Matusik3 Hanspeter Pfister
• Keywords: Computer Vision, ICCV 2009, Image Restoration