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Edge-Based Defocus Blur Estimation With Adaptive Scale Selection

Edge-Based Defocus Blur Estimation With Adaptive Scale Selection

Abstract:

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.

Existing System:

One of the main drawbacks of existing edge-based methods is that the mathematical formulation of the blur model assumes isolated edges, while natural images tend to present complex, interfering edges. Also, most existing techniques rely on the slow alpha Laplacian matting scheme (or variations) for obtaining the dense blur map, which compromises the execution time.

Proposed System:

We propose a fast and accurate edge-based method for defocus blur map estimation from a single image based on reblurred gradient magnitude ratios, aiming to overcome the two main problems mentioned in the previous paragraph. The coreĀ  of the proposed approach is to estimate a scale-consistent edge map along with a local scale parameter that indicates how isolated each detected edge is. The local scale is used to adaptively select a reblurring parameter accounting for noise, edge interference and mis-localization, generating an initial blur estimate. We then introduce a Connected Edge Filter (CEF) to regularize the sparse map, enforcing spatial consistency of adjacent edge pixels along an edge contour. Finally, a fast image guided propagation scheme is used to obtain a dense map.

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