Abstract In common natural image blur, objects that not lie in the focal length of a digital camera generate defocus areas in the photographed image. In this paper, we propose a novel edge-based method for spatially varying defocus blur detection based on reblurred DCT coefficients ratios of the corresponding local patches. This method selects appropriate local reblur scales while detecting the edge points to deal with the problem of different blur degree and texture richness of the local image blocks. A sub-band fusion method of DCT coefficients is proposed to expand the difference between DCT features of in-focus and out-of-focus regions. Edge points blur maps are computed in multi-scale and multi-orientation image windows and more blur points are added to initialize sparse blur maps, finally Matting Laplacian method is used along with multi-scale fusion algorithm to obtain a more accurate blur segmentation. Experimental results present the proposed method has strong advantages in image detail processing and outperforms state-of-the-art methods for blur detection.

中文翻译：

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通过局部补丁的边缘像素 DCT 特征进行散焦模糊检测

摘要 在常见的自然图像模糊中，不在数码相机焦距内的物体会在拍摄的图像中产生散焦区域。在本文中，我们提出了一种新的基于边缘的方法，用于基于相应局部补丁的再模糊 DCT 系数比率的空间变化散焦模糊检测。该方法在检测边缘点的同时选择合适的局部再模糊尺度来处理局部图像块不同模糊程度和纹理丰富度的问题。提出了一种DCT系数的子带融合方法，以扩大焦内和离焦区域的DCT特征之间的差异。在多尺度和多方向图像窗口中计算边缘点模糊图，并添加更多模糊点以初始化稀疏模糊图，最后将Matting Laplacian 方法与多尺度融合算法一起使用以获得更准确的模糊分割。实验结果表明，所提出的方法在图像细节处理方面具有很强的优势，并且优于最先进的模糊检测方法。