Understanding and improving the realism of image composites
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Fast adaptive edge-aware mask generation
Proceedings of Graphics Interface 2012
Content-Aware Automatic Photo Enhancement
Computer Graphics Forum
Context-based automatic local image enhancement
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Automatic exposure correction of consumer photographs
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Technical Section: Automatic color realism enhancement for computer generated images
Computers and Graphics
Optimizing color consistency in photo collections
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Inverse image editing: recovering a semantic editing history from a before-and-after image pair
ACM Transactions on Graphics (TOG)
Data-driven hallucination of different times of day from a single outdoor photo
ACM Transactions on Graphics (TOG)
Style-based tone mapping for HDR images
SIGGRAPH Asia 2013 Technical Briefs
Hi-index | 0.00 |
Adjusting photographs to obtain compelling renditions requires skill and time. Even contrast and brightness adjustments are challenging because they require taking into account the image content. Photographers are also known for having different retouching preferences. As the result of this complexity, rule-based, one-size-fits-all automatic techniques often fail. This problem can greatly benefit from supervised machine learning but the lack of training data has impeded work in this area. Our first contribution is the creation of a high-quality reference dataset. We collected 5,000 photos, manually annotated them, and hired 5 trained photographers to retouch each picture. The result is a collection of 5 sets of 5,000 example input-output pairs that enable supervised learning. We first use this dataset to predict a user's adjustment from a large training set. We then show that our dataset and features enable the accurate adjustment personalization using a carefully chosen set of training photos. Finally, we introduce difference learning: this method models and predicts difference between users. It frees the user from using predetermined photos for training. We show that difference learning enables accurate prediction using only a handful of examples.