激光与光电子学进展, 2020, 57 (4): 041505, 网络出版: 2020-02-20   

基于显著性强度和梯度先验的多尺度图像盲去模糊 下载: 990次

Multi-Scale Image Blind Deblurring Based on Salient Intensity and a priori Gradient
作者单位
1 河海大学物联网工程学院, 江苏 常州 213022
2 中国工程物理研究院流体物理研究所, 四川 绵阳 621900
摘要
针对目前大多数基于统计先验的图像盲去模糊方法对边缘和细节恢复能力有限的问题,提出一种新的盲去模糊算法。通过降采样对模糊图像进行金字塔分解。在每一层图像上,利用显著性强度先验提取图像的边缘信息,并结合梯度低秩先验抑制图像中的模糊和噪声干扰。在多尺度上由粗到精地交替迭代模糊核和中间潜像,得到最终的准确模糊核。采用非盲去卷积方法复原出清晰图像。此外,针对多尺度迭代时间较长的问题,提出了一种自适应迭代策略,通过评估估计模糊核的相似性来调整迭代次数,有效减少计算成本。实验结果表明,本文算法可以准确地估计出模糊核,有效地抑制噪声影响,且得到的复原图像中含有更丰富的边缘和细节等特征。
Abstract
Most of the existing statistical a priori image blind deblurring methods have limited edge and detail recovery ability. To solve this problem, we proposed a new blind deblurring algorithm. First, by using the downsampling, the multi-scale decomposition of an image was performed based on pyramid decomposition. Then, in each image layer, the significant intensity a priori was used to extract the image edge, and the low gradient rank a priori was employed to suppress the blurring effect and noise. Next, the coarse-to-fine strategy was used to alternatively iterate the blur kernel and latent image to obtain an accurate final blur kernel. Finally, a clear image was recovered by a non-blind deconvolution method. Further, to reduce the iteration time of the multi-scale iteration, an adaptive iterative strategy was proposed. In this strategy, the number of iterations was adjusted by the similarity evaluation of the estimated blur kernels, and the computational cost was effectively reduced. The experimental results show that the proposed algorithm can accurately estimate the blur kernel and effectively suppress the influence of noise; also, the recovered image contains more edge and detail information.

陈晨, 许金鑫, 危才华, 李庆武. 基于显著性强度和梯度先验的多尺度图像盲去模糊[J]. 激光与光电子学进展, 2020, 57(4): 041505. Chen Chen, Jinxin Xu, Caihua Wei, Qingwu Li. Multi-Scale Image Blind Deblurring Based on Salient Intensity and a priori Gradient[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041505.

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