图像渐变
形态梯度
图像(数学)
振铃人工制品
图像分辨率
转化(遗传学)
人工智能
图像复原
计算机科学
参数统计
梯度分析
计算机视觉
图像处理
数学
特征检测(计算机视觉)
机器学习
化学
排序
统计
基因
生物化学
作者
Jian Sun,Zongben Xu,Heung-Yeung Shum
出处
期刊:IEEE transactions on image processing
[Institute of Electrical and Electronics Engineers]
日期:2011-06-01
卷期号:20 (6): 1529-1542
被引量:250
标识
DOI:10.1109/tip.2010.2095871
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI