Boundary finding based multi-focus image fusion through multi-scale morphological focus-measure

In this paper, we propose the first end-to-end convolutional neural network (CNN) architecture, Defocus Map Estimation Network (DMENet), for spatially varying defocus map estimation. To train the network, we produce a novel depth-of-field (DOF) dataset, SYNDOF, where each image is synthetically blurred with a ground-truth depth map. Due to the synthetic nature of SYNDOF, the feature characteristics of images in SYNDOF can differ from those of real defocused photos. To address this gap, we use domain adaptation that transfers the features of real defocused photos into those of synthetically blurred ones. Our DMENet consists of four subnetworks: blur estimation, domain adaptation, content preservation, and sharpness calibration networks. The subnetworks are connected to each other and jointly trained with their corresponding supervisions in an end-to-end manner. Our method is evaluated on publicly available blur detection and blur estimation datasets and the results show the state-of-the-art performance.In this paper, we propose the first end-to-end convolutional neural network (CNN) architecture, Defocus Map Estimation Network (DMENet), for spatially varying defocus map estimation. To train the network, we produce a novel depth-of-field (DOF) dataset, SYNDOF, where each image is synthetically blurred with a ground-truth depth map. Due to the synthetic nature of SYNDOF, the feature characteristics of images in SYNDOF can differ from those of real defocused photos. To address this gap, we use domain adaptation that transfers the features of real defocused photos into those of synthetically blurred ones. Our DMENet consists of four subnetworks: blur estimation, domain adaptation, content preservation, and sharpness calibration networks. The subnetworks are connected to each other and jointly trained with their corresponding supervisions in an end-to-end manner. Our method is evaluated on publicly available blur detection and blur estimation datasets and the results show the state-of-the-art performance.

/pdf/deep-defocus-map-estimation-using-domain-adaptation-51io4tg19p.pdf

Deep Defocus Map Estimation Using Domain Adaptation

The detection of spatially-varying blur without having any information about the blur type is a challenging task. In this paper, we propose a novel effective approach to address this blur detection problem from a single image without requiring any knowledge about the blur type, level, or camera settings. Our approach computes blur detection maps based on a novel High-frequency multiscale Fusion and Sort Transform (HiFST) of gradient magnitudes. The evaluations of the proposed approach on a diverse set of blurry images with different blur types, levels, and contents demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods qualitatively and quantitatively.

/pdf/spatially-varying-blur-detection-based-on-multiscale-fused-yiwov70y06.pdf

Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes

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.

Edge-Based Defocus Blur Estimation With Adaptive Scale Selection

Blur exists in many digital images, it can be mainly categorized into two classes: defocus blur which is caused by optical imaging systems and motion blur which is caused by the relative motion between camera and scene objects. In this letter, we propose a simple yet effective automatic blurred image region detection method. Based on the observation that blur attenuates high-frequency components of an image, we present a blur metric based on the log averaged spectrum residual to get a coarse blur map. Then, a novel iterative updating mechanism is proposed to refine the blur map from coarse to fine by exploiting the intrinsic relevance of similar neighbor image regions. The proposed iterative updating mechanism can partially resolve the problem of differentiating an in-focus smooth region and a blurred smooth region. In addition, our iterative updating mechanism can be integrated into other image blurred region detection algorithms to refine the final results. Both quantitative and qualitative experimental results demonstrate that our proposed method is more reliable and efficient compared to various state-of-the-art methods.

A Spectral and Spatial Approach of Coarse-to-Fine Blurred Image Region Detection

We present an effective method for defocus map estimation from a single natural image. It is inspired by the observation that defocusing can significantly affect the spectrum amplitude at the object edge locations in an image. By establishing the relationship between the amount of spatially varying defocus blur and spectrum contrast at edge locations, we first estimate the blur amount at these edge locations, then a full defocus map can be obtained by propagating the blur amount at edge locations over the entire image with a nonhomogeneous optimization procedure. The proposed method takes into consideration not only the affect of light refraction but also the blur texture of an image. Experimental results demonstrate that our proposed method is more reliable in defocus map estimation compared to various state-of-the-art methods.

Chunping Hou

Papers

Defocus map estimation from a single image via spectrum contrast.