# Analysis of Blur Measure Operators for Single Image Blur Segmentation
## 2 Related Work  
Most of the images captured using optical imaging systems usually contain two types of the regions: blurred and sharp. Blur can be categorized mainly into two types: (a) defocus blur, which is caused by the optical imaging system and (b) motion blur, which is caused by the relative motion between camera and scene objects. Blur in the image causes the deterioration of the image quality in that region. Therefore, it is important to detect and eliminate the blur from the images. Blur detection techniques can be divided broadly into two classes: (1) techniques that need only one image [9; 10; 11; 12; 13; 14; 15] and (2) techniques that use multiple images [20; 21]. Though, high-quality image can be recovered through multi-image scheme, yet it is a very challenging task as it requires to align the images, and thus it poses concerns on its applicability. Moreover, our study is restricted to techniques for single image. [PERSON] et al. differentiate between blurred and sharp regions based on the features like image gradient, spectra in frequency domain and data driven local filters in a multiscale scheme [11]. [PERSON] and [PERSON] focused on the blurriness of the edge pixels by employing multiscale edge detector and then propagated this measure to the rest of the image [5]. [PERSON] and [PERSON] measured the image blur by calculating first and second order gradients on the edges [22]. [PERSON] and [PERSON] employed the inhomogenous inversion heat diffusion to measure the defocus blur on the edges and propagated them through graph-cuts [23]. [PERSON] and [PERSON] estimated the defocus blur in [24] by using a local contrast prior on edge gradient magnitudes and then propagated through Markov random field (MRF). [PERSON] and [PERSON] looked for the Gaussian blur on edge pixels and then interpolated through matting Laplacian for non-edge pixels [25]. [PERSON] et al. measured the pixel blur by calculating the difference between before and after multi-scale Gaussian smoothing [26]. Then, blur map is refined by employing morphological reconstruction and guided filtering. [PERSON] and [PERSON] measured the blurriness by exploiting the double discrete wavelet transform [27]. [PERSON] et al. used localized Fourier spectrum to measure the probability of blur scale for each pixel and then implemented a constrained pairwise energy function [28]. [PERSON] et al. estimated the slight noticeable blur by utilizing sparse representation of image neighborhood windows in dictionary method and this blur is further smoothed by an edge-preserving fitler [12]. [PERSON] et al. estimated local blur by analyzing local and global gradient statistics [29]. [PERSON] et al. restored the natural images through their spectrum by estimating the parameters for defocus and motion blur kernels [30]. [PERSON] et al. proposed a general framework to retrieve a course blur map for both defocus and motion blurred images by using the log averaged spectrum residual of the image, and then updated it iteratively to achieve the fine map by exploiting the intrinsic relevance of the neighboring similar regions of the image [13]. [PERSON] and [PERSON] proposed a blur measure operator based on the difference between the distributions of uniform local binary patterns (LBP) of the blurred and proposed a robust algorithm to segment the defocus blur [14]. [PERSON] and [PERSON] detected the spatially varying blur by applying multiscale fusion of the high frequency Discrete Cosine Transform (DCT) coefficients [31]. [PERSON] et al. used a mixture of Gaussians to model the heavy-tailed natural image gradients and estimated the likelihood of spatially varying directional motion blur in the image using local Fourier transform [32]. Spatially uniform motion deblurring techniques are discussed in [33; 34; 35; 36; 37; 38]. Most of the uniform deblurring techniques have difficulty in handling non-uniform blurred images. [PERSON] et al. and [PERSON] et al.