Image Sharpening With Blur Map Estimation Using Convolutional Neural Network

Nasonov, Andrey; Krylov, Andrey S.; Lyukov, Dmitry

doi:10.5194/isprs-archives-xlii-2-w12-161-2019

Cited by 2 publications

(4 citation statements)

References 18 publications

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“…It allows the viewer to have a rich appreciation of the 3D space [2]. In computer vision, the defocus blur is used in a wide range of applications, such as deblurring [3][4][5], blur magnification [6], image quality assessment [7,8], image sharpening [9,10] and depth estimation [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…The use of DNNs for defocus blur estimation or detection has also been studied. In [9], a convolutional neural network (CNN) is used to estimate the optimum values for the grid wrapping algorithm (GWIS) [43] for image sharpening. The CNN is trained using blurred patches to estimate the optimum parameter based on blurriness to achieve the best sharpening result.…”

Section: Introductionmentioning

confidence: 99%

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Single image defocus map estimation through patch blurriness classification and its applications

Galetto

Deng

2022

Vis Comput

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Depth information is useful in many image processing applications. However, since taking a picture is a process of projection of a 3D scene onto a 2D imaging sensor, the depth information is embedded in the image. Extracting the depth information from the image is a challenging task. A guiding principle is that the level of blurriness due to defocus is related to the distance between the object and the focal plane. Based on this principle and the widely used assumption that Gaussian blur is a good model for defocus blur, we formulate the problem of estimating the spatially varying defocus blurriness as a Gaussian blur classification problem. We solve the problem by training a deep neural network to classify image patches into one of the 20 levels of blurriness. We have created a dataset of more than 500,000 image patches of size $$32\times 32$$ 32 × 32 which does not require human labelling. The dataset is used to train and test several well-known network models. We find that MobileNetV2 is suitable for this application due to its low memory requirement and high accuracy. The trained model is used to determine the patch blurriness which is then refined by applying an iterative weighted guided filter. The result is a defocus map that carries the information of the degree of blurriness for each pixel. We compare the proposed method with state-of-the-art techniques and we demonstrate its successful applications in adaptive image enhancement and defocus magnification limited to images that present a clear distinction between defocus levels.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Single image defocus map estimation through patch blurriness classification and its applications

Galetto

Deng

2022

Vis Comput

View full text Add to dashboard Cite

show abstract

“…It allows the viewer to have a rich appreciation of the 3D space [2]. In computer vision, the defocus blur is used in a wide range of applications such as deblurring [3][4][5], blur magnification [6], image quality assessment [7,8], image sharpening [9,10] and depth estimation [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…The use of DNNs for defocus blur estimation or detection has also been studied. In [9], a CNN is used to estimate the optimum values for the grid wrapping algorithm (GWIS) [35] for image sharpening. The CNN is trained using blurred patches to estimate the optimum parameter based on blurriness to achieve the best sharpening result.…”

Section: Introductionmentioning

confidence: 99%

Single image deep defocus estimation and its applications

Galetto

Deng

2021

Preprint

View full text Add to dashboard Cite

The depth information is useful in many image processing applications. However, since taking a picture is a process of projection of a 3D scene onto a 2D imaging sensor, the depth information is embedded in the image. Extracting the depth information from the image is a challenging task. A guiding principle is that the level of blurriness due to defocus is related to the distance between the object and the focal plane. Based on this principle and the widely used assumption that Gaussian blur is a good model for defocus blur, we formulate the problem of estimating the spatially varying defocus blurriness as a Gaussian blur classification problem. We solved the problem by training a deep neural network to classify image patches into one of the 20 levels of blurriness. We have created a dataset of more than 500000 image patches of size 32x32 which are used to train and test several well-known network models. We find that MobileNetV2 is suitable for this application due to its low memory requirement and high accuracy. The trained model is used to determine the patch blurriness which is then refined by applying an iterative weighted guided filter. The result is a defocus map that carries the information of the degree of blurriness for each pixel. We compare the proposed method with state-of-the-art techniques and we demonstrate its successful applications in adaptive image enhancement, defocus magnification, and multi-focus image fusion.

show abstract

Image Sharpening With Blur Map Estimation Using Convolutional Neural Network

Cited by 2 publications

References 18 publications

Single image defocus map estimation through patch blurriness classification and its applications

Single image defocus map estimation through patch blurriness classification and its applications

Single image deep defocus estimation and its applications

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