Single parameter post-processing method for image deblurring

Krylov, Andrey S.; Nasonov, Andrey; Pchelintsev, Yakov

doi:10.1109/ipta.2017.8310093

Cited by 4 publications

(5 citation statements)

References 15 publications

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“…For the purpose of image restoration, image warping methods were proposed in [1], [20], [28], especially aimed toward image deblurring. The basic assumption in these techniques is that the blurring process distorts the edge by shifting the pixels away from the true edge.…”

Section: Image Restoration By Grid Warpingmentioning

confidence: 99%

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Deep Convolutional Grid Warping Network for Joint Depth Map Upsampling

Yang

Kim

2020

IEEE Access

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Depth maps play an important role in the representation of 3D information. They are often simultaneously acquired with color images; however, their resolution is significantly lower than that of color images owing to hardware limitations. In this paper, we propose a novel approach to upsample depth maps by using geometric deformation instead of pixel value refinement, which is employed in a majority of existing methods. This approach, known as grid warping, displaces the position of blurred pixels around the edge towards the center of the edge. The displacement vector for warping is obtained from an analysis of the corresponding high-resolution color image. Furthermore, we propose an edge signal and displacement vector modeling for a more effective analysis. The experimental results show that the proposed method significantly improves the quantitative and visual performance, as compared to state-of-the-art methods. The source codes of the proposed method will be available at https://github.com/yym064/DeepGridWarp.

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Section: Image Restoration By Grid Warpingmentioning

confidence: 99%

“…As stated in Section II, the previous approaches in [20], [28] attempted to localize the true edge before applying grid warping. This is because it determines the image warping direction around the true edge and significantly affects overall performance.…”

Section: Network Architecturementioning

confidence: 99%

Deep Convolutional Grid Warping Network for Joint Depth Map Upsampling

Yang

Kim

2020

IEEE Access

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“…The grid warping algorithm is described in detail in (Nasonova and Krylov, 2015, Krylov et al, 2017, Nasonov et al, 2018. We present here a brief explanation of this algorithm.…”

Section: Grid Warpingmentioning

confidence: 99%

“…It has been shown that GWIS algorithm is robust to blur estimation errors (Krylov et al, 2017), and the decrease of image quality due to inaccurately estimated blur parameter is proportional to the relative difference between the estimated and the optimal blur parameters but not to the absolute difference between them.…”

Section: Improvementsmentioning

confidence: 99%

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Image Sharpening With Blur Map Estimation Using Convolutional Neural Network

Nasonov

Krylov

Lyukov

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> We propose a method for choosing optimal values of the parameters of image sharpening algorithm for out-of-focus blur based on grid warping approach. The idea of the considered sharpening algorithm is to move pixels from the edge neighborhood towards the edge centerlines. Compared to traditional deblurring algorithms, this approach requires only scalar blur level value rather than a blur kernel. We propose a convolutional neural network based algorithm for estimating the blur level value.</p>

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Image Sharpening by Grid Warping with Curvature Analysis

Nasonov

Krylov

2019

2019 15th International Conference on Signal-Image Technology &Amp; Internet-Based Systems (SITIS)

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Single parameter post-processing method for image deblurring

Cited by 4 publications

References 15 publications

Deep Convolutional Grid Warping Network for Joint Depth Map Upsampling

Deep Convolutional Grid Warping Network for Joint Depth Map Upsampling

Image Sharpening With Blur Map Estimation Using Convolutional Neural Network

Image Sharpening by Grid Warping with Curvature Analysis

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