Image super-resolution based wavelet framework with gradient prior
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Fast computation of edge model representation for image sequence super-resolution
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
A particle filter framework for contour detection
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
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In this paper, we propose a novel generic image prior-gradient profile prior, which implies the prior knowledge of natural image gradients. In this prior, the image gradients are represented by gradient profiles, which are 1-D profiles of gradient magnitudes perpendicular to image structures. We model the gradient profiles by a parametric gradient profile model. Using this model, the prior knowledge of the gradient profiles are learned from a large collection of natural images, which are called gradient profile prior. Based on this prior, we propose a gradient field transformation to constrain the gradient fields of the high resolution image and the enhanced image when performing single image super-resolution and sharpness enhancement. With this simple but very effective approach, we are able to produce state-of-the-art results. The reconstructed high resolution images or the enhanced images are sharp while have rare ringing or jaggy artifacts.