Combining Weighted Total Variation and Deep Image Prior for natural and medical image restoration via ADMM

Cascarano, Pasquale; Sebastiani, Andrea; Comes, Maria Colomba; Franchini, Giorgia; Porta, Federica

doi:10.1109/iccsa54496.2021.00016

Cited by 40 publications

(22 citation statements)

References 19 publications

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“…( 8) is similar to the traditional DIP solution by forcing Df θ ðx 0 Þ to approach v k − u k ∕β with the neural network; here the parameter θ is optimized by a gradient descent method. 27,28 In Eqs. ( 9) and ( 10), the second-order derivatives in different directions are separately processed.…”

Section: Methodsmentioning

confidence: 99%

Untrained neural network enhances the resolution of structured illumination microscopy under strong background and noise levels

Yao

Huang

et al. 2023

Adv. Photon. Nexus

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Structured illumination microscopy (SIM) has been widely applied in the superresolution imaging of subcellular dynamics in live cells. Higher spatial resolution is expected for the observation of finer structures. However, further increasing spatial resolution in SIM under the condition of strong background and noise levels remains challenging. Here, we report a method to achieve deep resolution enhancement of SIM by combining an untrained neural network with an alternating direction method of multipliers (ADMM) framework, i.e., ADMM-DRE-SIM. By exploiting the implicit image priors in the neural network and the Hessian prior in the ADMM framework associated with the optical transfer model of SIM, ADMM-DRE-SIM can further realize the spatial frequency extension without the requirement of training datasets. Moreover, an image degradation model containing the convolution with equivalent point spread function of SIM and additional background map is utilized to suppress the strong background while keeping the structure fidelity. Experimental results by imaging tubulins and actins show that ADMM-DRE-SIM can obtain the resolution enhancement by a factor of ∼1.6 compared to conventional SIM, evidencing the promising applications of ADMM-DRE-SIM in superresolution biomedical imaging.

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Section: Methodsmentioning

confidence: 99%

Untrained neural network enhances the resolution of structured illumination microscopy under strong background and noise levels

Yao

Huang

et al. 2023

Adv. Photon. Nexus

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“…For the percentage of noise in the biomedical image, a method known in the literature for estimating noise was used, arriving at a percentage of noise just under five percent. As for the qualitative assessment of the images, we relied on a literature paper [7] where the same image is already used, with specific details referenced to evaluate its accuracy.…”

Section: Experiments On the Real Ct Imagementioning

confidence: 99%

“…The chosen Autoencoder [5,7] belongs to the DIP category, and it is prone to semi-convergence. Regarding the choice of the CNN architecture, in particular the autoencoder architecture, in this work we fix it as one of the networks proposed in Ulyanov et al [5], that is an autoencoder with five downsampling and five bilinear upsampling layers with convolutional skip connections.…”

mentioning

confidence: 99%

Deep Image Prior for medical image denoising, a study about parameter initialization

Sapienza

Franchini

Govi

et al. 2022

Front. Appl. Math. Stat.

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Convolutional Neural Networks are widely known and used architectures in image processing contexts, in particular for medical images. These Deep Learning techniques, known for their ability to extract high-level features, almost always require a labeled dataset, a process that can be computationally expensive. Most of the time in the biomedical context, when images are used they are noisy and the ground-truth is unknown. For this reason, and in the context of Green Artificial Intelligence, recently, an unsupervised method that employs Convolutional Neural Networks, or more precisely autoencoders, has appeared in the panorama of Deep Learning. This technique, called Deep Image Prior (DIP) by the authors, can be used in areas such as denoising, superresolution, and inpainting. Starting from these assumptions, this work analyses the robustness of these networks with respect to different types of initialization. First of all, we analyze the different types of parameters: related to the Batch Norm and the Convolutional layers. For the results, we focus on the speed of convergence and the maximum performance obtained. However, this paper aims to apply acquired information on Computer Tomography noised images. In fact, the final purpose is to test the best initializations of the first phase on a phantom image and then on a real Computer Tomography one. In fact, Computer Tomography together with Magnetic Resonance Imaging and Positron Emission Tomography are some of the diagnostic tools currently available to neuroscientists and oncologists. This work shows how initializations affect final performances and, in addition, how they should be used in the medical image reconstruction field. The section on numerical experiments shows results that on the one hand confirm the importance of a good initialization to obtain fast convergence and high performance; on the other hand, it shows how the method is robust to the processing of different image types: natural and medical. Not a single good initialization is discovered, but many of them could be chosen, according to specific necessities of the single problem.

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“…The second term occurring in the minimization is the so-called regularization term, which has the role of considering some characteristics of the desired image and controlling the influence of the noise in the reconstruction. Several options are available for such function:

norm for diffuse components [ 6 ],

for promoting sparse solution [ 7 , 8 ], Total Variation functional [ 9 , 10 , 11 ] (or its smooth counterpart [ 12 ]) for edge preserving. One may consider a composite regularization function, such as the Elastic–Net [ 13 ], or even non-convex options are available [ 3 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

upU-Net Approaches for Background Emission Removal in Fluorescence Microscopy

Benfenati

2022

J. Imaging

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The physical process underlying microscopy imaging suffers from several issues: some of them include the blurring effect due to the Point Spread Function, the presence of Gaussian or Poisson noise, or even a mixture of these two types of perturbation. Among them, auto–fluorescence presents other artifacts in the registered image, and such fluorescence may be an important obstacle in correctly recognizing objects and organisms in the image. For example, particle tracking may suffer from the presence of this kind of perturbation. The objective of this work is to employ Deep Learning techniques, in the form of U-Nets like architectures, for background emission removal. Such fluorescence is modeled by Perlin noise, which reveals to be a suitable candidate for simulating such a phenomenon. The proposed architecture succeeds in removing the fluorescence, and at the same time, it acts as a denoiser for both Gaussian and Poisson noise. The performance of this approach is furthermore assessed on actual microscopy images and by employing the restored images for particle recognition.

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Combining Weighted Total Variation and Deep Image Prior for natural and medical image restoration via ADMM

Cited by 40 publications

References 19 publications

Untrained neural network enhances the resolution of structured illumination microscopy under strong background and noise levels

Untrained neural network enhances the resolution of structured illumination microscopy under strong background and noise levels

Deep Image Prior for medical image denoising, a study about parameter initialization

upU-Net Approaches for Background Emission Removal in Fluorescence Microscopy

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