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Personal Photo Enhancement
Using Example Images
ACM Transactions on Graphics, April 2010. Presented at SIGGRAPH 2010.
Neel Joshi      Wojciech Matusik      Edward H. Adelson      David J. Kriegman
Microsoft Research, UCSD, Adobe      Disney Research, Adobe, MERL      MIT CSAIL      UCSD

Automatically correcting personal photos. We automatically enhance images using prior examples of “good” photos of a person. Here we deblur a blurry photo of a person where the blur is unknown. Using a set of other sharp images of the same person as priors (left), we automatically solve for the unknown blur kernel and deblur the original photo (middle) to produce a sharp image (right); the recovered blur kernel is shown in the top right enlarged 3×.


We describe a framework for improving the quality of personal photos by using a person’s favorite photographs as examples. We observe that the majority of a person’s photographs include the faces of a photographer’s family and friends and often the errors in these photographs are the most disconcerting. We focus on correcting these types of images and use common faces across images to automatically perform both global and face-specific corrections. Our system achieves this by using face detection to align faces between “good” and “bad” photos such that properties of the good examples can be used to correct a bad photo. These “personal” photos provide strong guidance for a number of operations and, as a result, enable a number of high-quality image processing operations.We illustrate the power and generality of our approach by presenting a novel deblurring algorithm, and we show corrections that perform sharpening, superresolution, in-painting of over- and underexposured regions, and white-balancing.


Automatically Deblurred using data from the Sensor Attachment (images are blinking between the blurred image and our deblurred result)

(3.51 MB)

(9.27 MB)

(16.4 MB)

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