Complex difffusion based image inpainting

Gopinath, N. S.; Arjun, K.; Shankar, J Adithya; Nair, Jyothisha J.

doi:10.1109/ngct.2015.7375266

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…Gopinath et al [18] proposed fourth-order complex diffusion-based image inpainting. This model improves the filling capacity of anisotropic diffusion process.…”

Section: ) Integer Calculus Based Modelsmentioning

confidence: 99%

“…These can be classified into four groups. These are diffusion based (generally called image inpainting) [1], [2], [3], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37] texture-based (generally called texture synthesis) [38], [39], hybrid-based [40], [41], [42], [43], and learning based image inpainting models [44], [45], [46], [47]…”

Section: Introductionmentioning

confidence: 99%

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A Qualitative Report on Diffusion based Image Inpainting Models

Sridevi¹,

Kumar²

2022

IJCDS

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Diffusion equations have been successfully applied in the field of digital image processing for the past twenty years, describing the random motion of the particles in physics. Image inpainting is a significant research problem in the image processing. Its main intent is to complete the unknown parts of the image from the knowledge of known parts of the image. This research problem can be used to restore damaged photograph, random loss of wavelet coefficients during transmission, superimposed text, noise, and/or blur. According to available models on digital image inpainting, this paper attempts to make an outline of state-of-the-art diffusion based image inpainting models with corresponding mathematical representation. We also compared the state-of-the-art diffusion based inpainting techniques in terms of its main idea, type of distortion, strengths, and weaknesses.

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“…Gopinath et al [18] proposed fourth-order complex diffusion-based image inpainting. This model improves the filling capacity of anisotropic diffusion process.…”

Section: ) Integer Calculus Based Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Qualitative Report on Diffusion based Image Inpainting Models

Sridevi¹,

Kumar²

2022

IJCDS

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“…The former one, known as image inpainting, is extensively explored first with a lot of efforts. The traditional methods include diffusion-based [1][2][3][4] and patch-based [5][6][7][8]. The diffusionbased approaches diffuse the undamaged part to the region to be filled by mimicking the process of diffusion in physics.…”

Section: Introductionmentioning

confidence: 99%

Edge‐aware image outpainting with attentional generative adversarial networks

Zhang

Feng

et al. 2022

IET Image Processing

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Image outpainting aims at extending the field of view of an existing image. While image inpainting has achieved great success with the deep learning technology, image outpainting still receive less attention. The main challenge is how to generate high-quality extended images with clear texture and highly consistent semantic information. In order to solve the problem of the effect of invalid pixels on the generated image and the distance of effective pixels is too far. This paper proposes a two-stage image outpainting method (the EA method), which consists of an edge generation stage and an edge transformation stage. In this paper, the convolutional block attention module (CBAM) is introduced into the generation network to focus on spatial and channel feature and the improved VAE-GAN structure is used to generate the extended image for more realistic semantics. The EA method is evaluated on the CelebA, Pairs-streetview and homemade landscapes dataset, and show that the results contain high-quality textures as well as faithfully extend the semantics. The average PSNR, SSIM, FID index of the EA method on the three datasets is 22.7961, 0.7061, 6.8553 and show that it outperforms existing algorithms in both quantitative and qualitative analysis.

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“…The image inpainting approaches are branched into the three groups: exemplar-based inpainting 1,2 , diffusionbased inpainting [3][4][5][6][7][8][9][10][11][12][13] , and hybrid inpainting 14 . Exemplar-based inpainting technique repeatedly synthesises the unknown area by a most identical patch in the known area.…”

Section: Introductionmentioning

confidence: 99%

Image Inpainting and Enhancement using Fractional Order Variational Model

Sridevi

Kumar

2017

Def. Sc. Jl.

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The intention of image inpainting is to complete or fill the corrupted or missing zones of an image by considering the knowledge from the source region. A novel fractional order variational image inpainting model in reference to Caputo definition is introduced in this article. First, the fractional differential, and its numerical methods are represented according to Caputo definition. Then, a fractional differential mask is represented in 8-directions. The complex diffusivity function is also defined to preserve the edges. Finally, the missing regions are filled by using variational model with fractional differentials of 8-directions. The simulation results and analysis display that the new model not only inpaints the missing regions, but also heightens the contrast of the image. The inpainted images have better visual quality than other fractional differential filters.

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Complex difffusion based image inpainting

Cited by 6 publications

References 7 publications

A Qualitative Report on Diffusion based Image Inpainting Models

A Qualitative Report on Diffusion based Image Inpainting Models

Edge‐aware image outpainting with attentional generative adversarial networks

Image Inpainting and Enhancement using Fractional Order Variational Model

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