Single image defocus estimation by modified gaussian function. Issue 6 (11th April 2019) - Hassan, Haseeb; Bashir, Ali Kashif; Abbasi, Rashid; Ahmad, Waqas; Luo, Bin
Abstract: This article presents an algorithm to estimate the defocus blur from a single image. Most of the existing methods estimate the defocus blur at edge locations, which further involves the reblurring process. For this purpose, existing methods use the traditional Gaussian function in the phase of reblurring but it is found that the traditional Gaussian kernel is sensitive to the edges and can cause loss of edges information. Hence, there are more chances of missing spatially varying blur at edge locations. We offer the repeated averaging filters as an alternative to the traditional Gaussian function, which is more effective, and estimate the spatially varying defocus blur at edge locations. By using repeated averaging filters, a blur sparse map is computed. The obtained sparse map is propagated by integration of superpixels segmentation and transductive inference to estimate full defocus blur map. Our adopted method of repeated averaging filters has less computational time of defocus blur map estimation and has better visual estimates of the final defocus recovered map. Moreover, it has surpassed many previous state‐of‐the‐art proposed systems in terms of quantative analysis. Abstract : We proposed the repeated averaging filters as an alternative to the traditional Gaussian function for single image defocus blur estimation. Most of the previous blur estimation techniques used the traditional Gaussian function. But traditional Gaussian function is sensitive to edges and can cause loss of edges information. So there are more chances of missing spatially varying blur at edge locations. Our adopted method of repeated averaging filters has overcome this problem and produced better visual estimates of the final defocus recovered map.