Edge-Based Defocus Blur Estimation With Adaptive Scale Selection.

Objects that do not lie at the focal distance of a digital camera generate defocused regions in the captured image. This paper presents a new edge-based method for spatially varying defocus blur estimation using a single image based on reblurred gradient magnitudes. The proposed approach initially computes a scale-consistent edge map of the input image and selects a local reblurring scale aiming to cope with noise, edge mis-localization, and interfering edges. An initial blur estimate is computed at the detected scale-consistent edge points and a novel connected edge filter is proposed to smooth the sparse blur map based on pixel connectivity within detected edge contours. Finally, a fast guided filter is used to propagate the sparse blur map through the whole image. Experimental results show that the proposed approach presents a very good compromise between estimation error and running time when compared with the state-of-the-art methods. We also explore our blur estimation method in the context of image deblurring, and show that metrics typically used to evaluate blur estimation may not correlate as expected with the visual quality of the deblurred image.