Blind Image Deblurring using the l0 Gradient Prior

Anger, Jérémy; Facciolo, Gabriele; Delbracio, Mauricio

doi:10.5201/ipol.2019.243

Cited by 22 publications

(17 citation statements)

References 30 publications

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“…We use the efficient implementation of Anger et al [12]. Even if the 0 gradient prior kernel estimation method was designed for text and natural images, we argue that it is applicable to satellite images without any adaptation.…”

Section: Blur Kernel Estimationmentioning

confidence: 99%

Assessing the Sharpness of Satellite Images: Study of the Planetscope Constellation

Anger

Franchis

Facciolo

2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

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New micro-satellite constellations enable unprecedented systematic monitoring applications thanks to their wide coverage and short revisit capabilities. However, the large volumes of images that they produce have uneven qualities, creating the need for automatic quality assessment methods. In this work, we quantify the sharpness of images from the PlanetScope constellation by estimating the blur kernel from each image. Once the kernel has been estimated, it is possible to compute an absolute measure of sharpness which allows to discard low quality images and deconvolve blurry images before any further processing. The method is fully blind and automatic, and since it does not require the knowledge of any satellite specifications it can be ported to other constellations.

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Section: Blur Kernel Estimationmentioning

confidence: 99%

Assessing the Sharpness of Satellite Images: Study of the Planetscope Constellation

Anger

Franchis

Facciolo

2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

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“…We adopted Lp-pseudonorm shrinkage to solve the Equation (14). In contrast to Wang et al, [21] where the value of p was the same, we separated the p values of different gradients and directions of the image.…”

Section: Z Imentioning

confidence: 99%

Infrared Image Deblurring via High-Order Total Variation and Lp-Pseudonorm Shrinkage

Yang

Chen

2020

Applied Sciences

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The quality of infrared images is affected by various degradation factors, such as image blurring and noise pollution. Anisotropic total variation (ATV) has been shown to be a good regularization approach for image deblurring. However, there are two main drawbacks in ATV. First, the conventional ATV regularization just considers the sparsity of the first-order image gradients, thus leading to staircase artifacts. Second, it employs the L1-norm to describe the sparsity of image gradients, while the L1-norm has a limited capacity of depicting the sparsity of sparse variables. To address these limitations of ATV, a high-order total variation is introduced in the ATV deblurring model and the Lp-pseudonorm is adopted to depict the sparsity of low- and high-order total variation. In this way, the recovered image can fit the image priors with clear edges and eliminate the staircase artifacts of the ATV model. The alternating direction method of multipliers is used to solve the proposed model. The experimental results demonstrate that the proposed method does not only remove blurs effectively but is also highly competitive against the state-of-the-art methods, both qualitatively and quantitatively.

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“…Our implementation is based on [28] which upscales the predicted sharp image by a factor two using bicubic interpolation. However, instead of 5 iterations per scale as performed in [28], our method requires only 2 iterations by warm-starting the second one using the the previous estimation of u. This allows to reduce the number of inner iterations required for the sharp prediction step by setting κ = 5 and β (0) u = 0.05 in (3) and (4).…”

Section: Kernel Estimationmentioning

confidence: 99%

Efficient blind deblurring under high noise levels

Anger

Delbracio

Facciolo

2019

2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA)

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The goal of blind image deblurring is to recover a sharp image from a motion blurred one without knowing the camera motion. Current state-of-the-art methods have a remarkably good performance on images with no noise or very low noise levels. However, the noiseless assumption is not realistic considering that low light conditions are the main reason for the presence of motion blur due to requiring longer exposure times. In fact, motion blur and high to moderate noise often appear together. Most works approach this problem by first estimating the blur kernel k and then deconvolving the noisy blurred image. In this work, we first show that current state-of-theart kernel estimation methods based on the 0 gradient prior can be adapted to handle high noise levels while keeping their efficiency. Then, we show that a fast non-blind deconvolution method can be significantly improved by first denoising the blurry image. The proposed approach yields results that are equivalent to those obtained with much more computationally demanding methods.

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Blind Image Deblurring using the l0 Gradient Prior

Cited by 22 publications

References 30 publications

Assessing the Sharpness of Satellite Images: Study of the Planetscope Constellation

Assessing the Sharpness of Satellite Images: Study of the Planetscope Constellation

Infrared Image Deblurring via High-Order Total Variation and Lp-Pseudonorm Shrinkage

Efficient blind deblurring under high noise levels

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