On Image Enhancement for Unsupervised Image Description and Matching
An image enhancer improves the visibility and readability of the content of any input image by modifying one or more features related to vision perception. Its performance is usually assessed by quantifying and comparing the level of these features in the input and output images and/or with respect to a gold standard, often regardless of the application in which the enhancer is invoked. Here we provide an empirical evaluation of six image enhancers in the specific context of unsupervised image description and matching. To this purpose, we use each enhancer as pre-processing step of the well known algorithms SIFT and ORB, and we analyze on a public image dataset how the enhancement influence image retrieval. Our analysis shows that improving perceptual features like image brightness, contrast and regularity increases the accuracy of SIFT and ORB. More generally, our study provides a scheme to evaluate image enhancement from an application viewpoint, promoting an aware usage of the evaluated enhancers in a specific computer vision framework.
The authors would like to thank Alessio Xompero (with Fondazione Bruno Kessler, IT) and (Queen Mary University, UK), for the fruitful discussions about this topic.
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