A new algorithm for image inpainting in Fourier transform domain

Mousavi, Parisa; Tavakoli, Ali

doi:10.1007/s40314-019-0761-4

Cited by 8 publications

(6 citation statements)

References 15 publications

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“…Subsequently, they are individually unfolded into𝑄 , , 𝐾 , , 𝑉 , in two dimensions. By applying the Squeeze operation on 𝑄 , , 𝐾 , , 𝑉 , , global information for each layer's feature map is obtained, as shown in Equation (6). .…”

Section: Multi-head Cseattention Modulementioning

confidence: 99%

“…The image is then transformed into the frequency domain for denoising and subsequently transformed back into the spatial domain to obtain the denoised image. The most commonly used frequency domain denoising methods are based on Fourier transform domain [6] and wavelet transform domain [7] . While these methods can to some extent remove noise, they often result in a significant loss of fine texture details in the image.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Remote sensing image denoising algorithm based on improved Transformer network

Wang,

wei,

wang

et al. 2024

2024 International Conference on Optoelectronic Information and Optical Engineering (OIOE 2024)

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Section: Multi-head Cseattention Modulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Remote sensing image denoising algorithm based on improved Transformer network

Wang,

wei,

wang

et al. 2024

2024 International Conference on Optoelectronic Information and Optical Engineering (OIOE 2024)

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“…Theljani et al [36] based on a fourth-order variational model, used an adaptive selection of the diffusion parameters to optimize the regularization effects in the neighborhoods of the small features. Mousavi et al [28] considered the effect of spectrum and phase angle of the Fourier transform, generated two regularization parameters and had two degree of freedom, so as to restore an image. These methods can be used to restore small-scale damaged regions, such as removing scratches, removing text coverage, filling holes, and so on.…”

Section: Related Workmentioning

confidence: 99%

An image inpainting method for object removal based on difference degree constraint

Zhang

Chang

2020

Multimed Tools Appl

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In the inpainting method for object removal, SSD (Sum of Squared Differences) is commonly used to measure the degree of similarity between the exemplar patch and the target patch, which has a very important impact on the restoration results. Although the matching rule is relatively simple, it is likely to lead to the occurrence of mismatch error. Even worse, the error may be accumulated along with the process continues. Finally some unexpected objects may be introduced into the target region, making the result unable to meet the requirements of visual consistency. In view of these problems, we propose an inpainting method for object removal based on difference degree constraint. Firstly, we define the MSD (Mean of Squared Differences) and use it to measure the degree of differences between corresponding pixels at known positions in the target patch and the exemplar patch. Secondly, we define the SMD (Square of Mean Differences) and use it to measure the degree of differences between the pixels at known positions in the target patch and the pixels at unknown positions in the exemplar patch. Thirdly, based on MSD and SMD, we define a new matching rule and use it to find the most similar exemplar patch in the source region. Finally, we use the exemplar patch to restore the target patch. Experimental results show that the proposed method can effectively prevent the occurrence of mismatch error and improve the restoration effect.

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“…This was done to enhance the regularization effects specifically in the vicinity of tiny features. In their study, Mousavi et al [4] examined the impact of the magnitude and phase of the Fourier transform on image restoration. They proposed the use of two regularization parameters and incorporated two degrees of freedom in their approach.…”

Section: Introductionmentioning

confidence: 99%

Development of Image Inpainting for object removal using Enhanced Patch Priority and Matching Measures

Rao,

Revathi,

Bhanusree

et al. 2024

E3S Web Conf.

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Image inpainting can be used to fix broken images and get rid of distracting elements. In exemplar based methods, patch priority computation and exemplar patch selection are crucial to the success of image inpainting technique. The dropping effect occurred in the highest patch priority computation and matching error in the best patch selection are the major issues in the exemplar inpaint methods. In this paper, the enhanced priority calculation technique is employed to avoid the dropping effect and introduced the new similarity measuring process, Mean Squared Error (MSD). The efficacy of the proposed techniques is estimated by comparing with the available methods in the literature qualitatively. It shows that proposed methods outperformed existing techniques.

show abstract

A new algorithm for image inpainting in Fourier transform domain

Cited by 8 publications

References 15 publications

Remote sensing image denoising algorithm based on improved Transformer network

Remote sensing image denoising algorithm based on improved Transformer network

An image inpainting method for object removal based on difference degree constraint

Development of Image Inpainting for object removal using Enhanced Patch Priority and Matching Measures

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