论文标题
盲图卷积使用Student's Priep与重叠组的稀疏性
Blind Image Deconvolution using Student's-t Prior with Overlapping Group Sparsity
论文作者
论文摘要
在本文中，我们解决了盲图形反卷积问题，即消除模糊形成信号降解的图像，而无需任何模糊内核。由于该问题是错误的，因此图像先验在准确的盲卷卷积中起着重要作用。传统图像先验假定过滤域中的系数很少。但是，这里假定在稀疏系数上存在其他结构。因此，我们为盲图像反卷积提出了新的问题制定，该问题通过将Student的T图像与重叠的组稀疏性结合在一起，利用了结构信息。提出的方法导致有效的盲卷算法优于其他最先进的算法。
In this paper, we solve blind image deconvolution problem that is to remove blurs form a signal degraded image without any knowledge of the blur kernel. Since the problem is ill-posed, an image prior plays a significant role in accurate blind deconvolution. Traditional image prior assumes coefficients in filtered domains are sparse. However, it is assumed here that there exist additional structures over the sparse coefficients. Accordingly, we propose new problem formulation for the blind image deconvolution, which utilizes the structural information by coupling Student's-t image prior with overlapping group sparsity. The proposed method resulted in an effective blind deconvolution algorithm that outperforms other state-of-the-art algorithms.
