## 2 Related Work  
Measuring edge elongation caused by blurred effects has played a significant role in the analysis of blurred images. In previous approaches, the blurring effect was detected and estimated by measuring the blur extent of edges in [4] or by fitting the gradient magnitude to a normal distribution along the edge direction where the standard deviation of the distribution and gradient magnitude was used as the blur measure as in [5]. [PERSON] et al. [6] presented a defocus blur estimation using a transformer encoder and edge detection method. They proposed a hybrid architecture of convolutional neural networks with an edge-guided aggregation module and a feature fusion module for defocus blur detection. [PERSON] et al. [7] investigated the defocus blur detection method to detect blurred areas in images. To address the problem of uneven pixel distribution at the edges of defocused regions, they deliberately separated the main labels to prior tokens, including a structure body region and a edge transfer detail region. [PERSON] et al. [8] investigated blur detection algorithms, including support vector machine filters, focus measure thresholding, and convolutional neural networks on blurred images. The estimation of blur filter and latent unblurred images relies on blind image deconvolution methods [9; 10; 11]. This type of estimation tries to solve a severely ill-posed problem. Most of recent proposed methods in image deblurring implement a spatially invariant blur in which it is assumed that all pixels in the input image are blurred by the same PSF. The partial blur problem was considered in some methods by assuming a blur kernel or with the help of user interaction [12; 13; 14]. For all these methods, the deblurring is successful if the PSF is correctly reconstructed. However, in practice, blind deconvolution usually performs unsatisfactorily, even by making restrictive assumptions on image and kernel structures. This problem becomes even more significant when the partial blurring effect in images is considered. Therefore, blind deconvolution methods are not appropriate for general blur detection in terms of efficiency and accuracy, especially for handling images in a large database. In photography, the low-depth-of-field technique focuses the camera only on an object of interest. In the presented methods in [15; 16], the object of interest was extracted automatically. However, the computed low-depth-of-field images contained out-of-focus backgrounds, making the methods inappropriate for general blur detection. [PERSON] et al. [17] extended the previous idea for image autosegmentation as an application of blur analysis. They generated the low-depth-of-field images by calculating an indicator, defined by the ratio of high-frequency wavelet coefficients of the central regions of the whole image. Accordingly, the method simply assumed that low-depth-of-field images contained focused objects near the image center and out-of-focus objects in surrounding pixels of the image. Thus, the method also did not suit general-purpose blur analysis. The most relevant research to this work is probably related to depth recovery from motion and defocus blurring effects. [PERSON] et al. [18] proposed a learning framework for motion and defocus deblurring networks. Their networks were trained for removing object blur as a by-product. [PERSON] et al. [19] presented depth recovery with a single camera scanner by applying focus blur and changing the aperture number. Their proposed model performed well with both sharp and blurred images in computational depth estimation up to a range of 3.3 m, regardless of whether the image was in focus or out of focus. [PERSON] et al. (2020) suggested a deep convolutional neural network to estimate the depth and image deblurring. [PERSON] et al. (2017) presented a novel technique to generate a more accurate depth map for dynamic scenes using a combination of defocus and motion cues.