Edge-aware smoothing through adaptive interpolation

Al-nasrawi, Mukhalad; Deng, Guang; Thai, Ba

doi:10.1007/s11760-017-1164-x

Cited by 13 publications

(6 citation statements)

References 28 publications

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“…Al‐nasrawi et al [21] proposed a pixel level edge‐aware smoothing technique that utilises the idea of interpolation between two images, which are the observed/original image and a smooth version of the observed/original image. The filtering process proceeds in an iterative fashion and the interpolation weights are updated in each iteration based on the residual between the observed/original image and the current estimate image.…”

Section: Related Workmentioning

confidence: 99%

“…In addition to edge‐aware smoothing, these filters are broadly utilised in numerous applications in image processing and computational photography. Examples include image de‐noising [12, 17], detail enhancement [3, 18], image fusion [19, 20], texture smoothing [11, 12, 21–23], single image haze removal [24], tone mapping of high dynamic range (HDR) images [2, 3, 10, 23, 25], anomaly detection in hyper‐spectral images [26], object classification accuracy enhancement in hyper‐spectral images [27], enhance the output of semantic segmentation algorithms [13], depth super‐resolution/up‐sampling [11, 13], image colourisation [3, 11, 13], image colour quantisation [12], scale‐space filtering [12, 22], style transfer [10, 12], optical flow estimation [10], compression artefacts removal [14, 22], content‐aware resizing, and stereo matching [10].…”

Section: Introductionmentioning

confidence: 99%

“…This work is motivated by the guided image filter (GIF) [4] and adaptive interpolation filter (AIF) [21]. The key idea of this work is that edge‐aware smoothing can be obtained by a local interpolation between the input image and a guidance image, which, in the simplest case, could be a linearly smoothed version of the input by using a Gaussian filter.…”

Section: Introductionmentioning

confidence: 99%

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Guided adaptive interpolation filter

Waheed¹,

Al-nasrawi

Deng³

2020

IET image process

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Guided adaptive interpolation filter

Waheed¹,

Al-nasrawi

Deng³

2020

IET image process

Self Cite

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“…Nearest-neighbor, bilinear and bicubic interpolation are classical fast polynomial-based approaches [12,18,21], which however present limitations especially in presence of edges [20]. Thus, in order to treat the resulting artifacts, adaptive techniques have been proposed [1,13,14].…”

Section: Introductionmentioning

confidence: 99%

Convergence analysis for image interpolation in terms of the cSSIM

Marchetti¹,

Santin²

2021

Preprint

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Assessing the similarity of two images is a complex task that has attracted significant efforts in the image processing community. The widely used Structural Similarity Index Measure (SSIM) addresses this problem by quantifying a perceptual structural similarity.In this paper we consider a recently introduced continuous SSIM (cSSIM), which allows one to analyze sequences of images of increasingly fine resolutions. We prove that this index includes the classical SSIM as a special case, and we provide a precise connection between image similarity measured by the cSSIM and by the L 2 norm.Using this connection, we derive bounds on the cSSIM by means of bounds on the L 2 error, and we even prove that the two error measures are equivalent in certain circumstances. We exploit these results to obtain precise rates of convergence with respect to the cSSIM for several concrete image interpolation methods, and we further validate these findings by many numerical experiments.This newly established connection paves the way to obtain novel insights into the features and limitations of the SSIM.

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“…Esta discussão leva ao conceito recente de suavização com reconhecimento de bordas (edge-aware smoothing -EAS), que pode ser implementado seguindo a linha de métodos de difusão não-linear ou, iterativamente, adicionar as informações de borda de volta a uma imagem suavizada [6].…”

Section: Introductionunclassified

Variational Method Integrating Edge Detection and Smoothing in Digital Images

Santos

Loula

Giraldi

et al. 2019

Anais Do XV Workshop De Visão Computacional (WVC 2019)

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There is a consensus in computer vision about the importance of the scale concept for edge extraction and for image smoothing or representation. In this paper we explore a variational approach that allows to put together edge detection and image smoothing in a uniﬁed linear scheme. Basically, the functional proposed by Mumford and Shah is re-written as an energy deﬁned with two arguments: the ﬁrst one representing smooth versions of the original image and the second one encompassing its edge set. We follow known results in the variational analysis to obtain a numerical scheme to minimize the energy. We apply Fourier analysis to verify that the iterative scheme converges to a low-pass representation of the original image in the ﬁrst argument and a high-pass signal in the other one. In the experimental results we show that the obtained scheme encourages intraregion image smoothing in preference to interregion blurring with edge localization at a desired scale.

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Edge-aware smoothing through adaptive interpolation

Cited by 13 publications

References 28 publications

Guided adaptive interpolation filter

Guided adaptive interpolation filter

Convergence analysis for image interpolation in terms of the cSSIM

Variational Method Integrating Edge Detection and Smoothing in Digital Images

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