Wyniki wyszukiwania - BazTech

1

Digital image restoration using SURF algorithm

Tammineni Shanmukhaprasanthi, Rayavarapu Swarajya Madhuri, Gottapu Sasibhushana Rao, Goswami Raj Kumar

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2024

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T. 14, nr 1

37--40

EN

In contemporary times, the preservation of scientific and creative endeavours often relies on the utilization of film and image archives, hence emphasizing the significance of image processing as a critical undertaking. Image inpainting refers to the process of digitally altering an image in a manner that renders the adjustments imperceptible to a viewer lacking knowledge of the original image. Image inpainting is a technique mostly employed to restore damaged regions within an image by utilizing information obtained from matching characteristics in relevant images. This process involves filling in the damaged areas and removing undesired objects. The SURF (Speeded Up Robust Feature) algorithm under consideration is partitioned into three primary phases. Firstly, the essential characteristics of the impaired image and the pertinent image are identified. In the second stage, the relationship between the damaged image and the relevant image is determined in terms of translation, scaling, and rotation. Ultimately, the destroyed area is reconstructed through the application of the inverse transformation. The quality assessment of inpainted images can be evaluated using metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Mean Squared Error (MSE). The experimental findings provide evidence that the suggested inpainting technique is effective in terms of both speed and quality.

PL

We współczesnych czasach utrwalanie dorobku naukowego i twórczego często opiera się na wykorzystaniu archiwów filmowych i obrazowych, co podkreśla znaczenie przetwarzania obrazu jako przedsięwzięcia krytycznego. Inpainting odnosi się do procesu cyfrowej zmiany obrazu w sposób, który sprawia, że korekty są niezauważalne dla widza nie znającego oryginalnego obrazu. Inpainting to technika stosowana najczęściej w celu przywracania uszkodzonych obszarów obrazu poprzez wykorzystanie informacji uzyskanych na podstawie dopasowania cech odpowiednich obrazów. Proces ten polega na wypełnieniu uszkodzonych obszarów i usunięciu niepożądanych obiektów. Rozważany algorytm SURF (Speeded Up Robust Feature) dzieli się na trzy główne fazy. Po pierwsze, identyfikowane są podstawowe cechy obrazu zaburzonego i obrazu istotnego. W drugim etapie określa się relację pomiędzy obrazem uszkodzonym a obrazem odpowiednim pod względem translacji, skalowania i rotacji. Ostatecznie zniszczony obszar rekonstruuje się poprzez zastosowanie transformacji odwrotnej. Ocenę jakości renowacji obrazów można ocenić za pomocą wskaźników, takich jak wskaźnik podobieństwa strukturalnego (SSIM), szczytowy stosunek sygnału do szumu (PSNR) i błąd średniokwadratowy (MSE). Wyniki eksperymentów dostarczają dowodów na to, że sugerowana technika renowacji jest skuteczna zarówno pod względem szybkości, jak i jakości.

2

Restoration of Remote Satellite Sensing Images using Machine and Deep Learning : a Survey

Abdellaoui Meriem, Benabdelkader Souad, Assas Ouarda

Machine Graphics and Vision

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2023

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Vol. 32, No. 2

147--167

EN

Remote sensing satellite images are affected by different types of degradation, which poses an obstacle for remote sensing researchers to ensure a continuous and trouble-free observation of our space. This degradation can reduce the quality of information and its effect on the reliability of remote sensing research. To overcome this phenomenon, the methods of detecting and eliminating this degradation are used, which are the subject of our study. The original aim of this paper is that it proposes a state of art of recent decade (2012-2022) on advances in remote sensing image restoration using machine and deep learning, identified by this survey, including the databases used, the different categories of degradation, as well as the corresponding methods. Machine learning and deep learning based strategies for remote sensing satellite image restoration are recommended to achieve satisfactory improvements.

3

Sub-pixel Based Forming of High-Resolution Images

Prudyus I., Tkachenko V., Lazko L., Fabirovskyy S.

Przegląd Elektrotechniczny

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2015

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R. 91, nr 7

45-48

EN

The paper deals with considering the possibility of improving the image forming quality of the objects and scenes in remote sensing systems that works in visible range of waves. The analysis of the influence of the pixel shape aperture on the image formation quality and the ability to improve resolution by sub-pixel processing using inverse filtering and Tikhonov regularization, to enhance high spatial frequencies are conducted.

PL

W pracy rozważa się możliwość poprawy jakości obrazowania obiektów i scen z wykorzystaniem systemów zdalnego sondowanie Ziemi, które działają w widzialnym zakresie długości fal. Przeprowadzono analiza wpływu kształtu apertura piksela na jakość obrazu i zdolność do zwiększenia rozdzielczości przez subpikseli przetwarzanie z wykorzystanie filtracji odwrotnego oraz regularyzacji Tichonowa w celu wzmocnienia wysokich częstotliwości przestrzennych.

4

An efficient algorithm for adaptive total variation based image decomposition and restoration

Liu X., Huang L.

International Journal of Applied Mathematics and Computer Science

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2014

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Vol. 24, no. 2

405--415

EN

With the aim to better preserve sharp edges and important structure features in the recovered image, this article researches an improved adaptive total variation regularization and H-1 norm fidelity based strategy for image decomposition and restoration. Computationally, for minimizing the proposed energy functional, we investigate an efficient numerical algorithm—the split Bregman method, and briefly prove its convergence. In addition, comparisons are also made with the classical OSV (Osher–Sole–Vese) model (Osher et al., 2003) and the TV-Gabor model (Aujol et al., 2006), in terms of the edge-preserving capability and the recovered results. Numerical experiments markedly demonstrate that our novel scheme yields significantly better outcomes in image decomposition and denoising than the existing models.

5

SURE-Based Projections Onto Convex Sets for Image Restoration

Deng C., Tian W., Wang S., Hu S., Rao W., Li Y.

Przegląd Elektrotechniczny

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2013

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R. 89, nr 5

167-170

EN

Projections onto convex sets (POCS) algorithms have been widely used for image restoration problem. However, the relaxation parameter of POCS is strongly data-dependent and difficult to tune. In this work we focus on optimally selecting such parameter in POCS algorithm for image restoration. A stein’s unbiased risk estimate (SURE) based POCS (SPOCS) for image restoration algorithm is proposed, in which SURE is used to determine an optimal value. Finally, the effectiveness of the optimality of the proposed parameter selection is tested by image restoration experiments.

PL

W artykule przedstawiono metodę optymalnego doboru parametru relaksacji dla algorytmu POCS, służącego do odtwarzania obrazów. W proponowanym rozwiązaniu (SPOCS) zastosowano estymator Stein’a (SURE), służący do wyznaczenia optymalnej wartości współczynnika lambda. Działania algorytmu zostało zbadane eksperymentalnie.

6

Application of the partitioning method to specific Toeplitz matrices

Stanimirović P., Miladinović M., Stojanović I., Miljković S.

International Journal of Applied Mathematics and Computer Science

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2013

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Vol. 23, no. 4

809--821

EN

We propose an adaptation of the partitioning method for determination of the Moore–Penrose inverse of a matrix augmented by a block-column matrix. A simplified implementation of the partitioning method on specific Toeplitz matrices is obtained. The idea for observing this type of Toeplitz matrices lies in the fact that they appear in the linear motion blur models in which blurring matrices (representing the convolution kernels) are known in advance. The advantage of the introduced method is a significant reduction in the computational time required to calculate the Moore–Penrose inverse of specific Toeplitz matrices of an arbitrary size. The method is implemented in MATLAB, and illustrative examples are presented.

7

A well-posed multiscale regularization scheme for digital image denoising

Prasath V. B. S.

International Journal of Applied Mathematics and Computer Science

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2011

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Vol. 21, no 4

769-777

EN

We propose an edge adaptive digital image denoising and restoration scheme based on space dependent regularization. Traditional gradient based schemes use an edge map computed from gradients alone to drive the regularization. This may lead to the oversmoothing of the input image, and noise along edges can be amplified. To avoid these drawbacks, we make use of a multiscale descriptor given by a contextual edge detector obtained from local variances. Using a smooth transition from the computed edges, the proposed scheme removes noise in flat regions and preserves edges without oscillations. By incorporating a space dependent adaptive regularization parameter, image smoothing is driven along probable edges and not across them. The well-posedness of the corresponding minimization problem is proved in the space of functions of bounded variation. The corresponding gradient descent scheme is implemented and further numerical results illustrate the advantages of using the adaptive parameter in the regularization scheme. Compared with similar edge preserving regularization schemes, the proposed adaptive weight based scheme provides a better multiscale edge map, which in turn produces better restoration.

8

Neural networks for medical image processing

Pieciak T., Jaworek J., Gorgon M.

Bio-Algorithms and Med-Systems

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2011

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Vol. 7, no. 4

101--110

EN

The proposed article presents the most common types of artificial neural networks used to be performed in the field of medical imaging. The first section describes the use of artificial neural networks in the preprocessing stage, restoration of noisy and distorted images and in conjunction with morphological operations. The second part presents the artificial neural networks in image segmentation problem, particularly in adaptive binarization threshold level selection and as a complement to the active contour method.

9

Extractive Support Vector Algorithm on Support Vector Machines for Image Restoration

Yao C-C., Yu P-T., Hung R-W.

Fundamenta Informaticae

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2009

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Vol. 90, nr 1-2

171-190

EN

The major problem of SVMs is the dependence of the nonlinear separating surface on the entire dataset which creates unwieldy storage problems. This paper proposes a novel design algorithm, called the extractive support vector algorithm, which provides improved learning speed and a vastly improved performance. Instead of learning and training with all input patterns, the proposed algorithm selects support vectors from the input patterns and uses these support vectors as the training patterns. Experimental results reveal that our proposed algorithmprovides near optimal solutions and outperforms the existing design algorithms. In addition, a significant framework which is based on extractive support vector algorithm is proposed for image restoration. In the framework, input patterns are classified by three filters: weighted order statistics filter, alpha-trimmed mean filter and identity filter. Our proposed filter can achieve three objectives: noise attenuation, chromaticity retention, and preservation of edges and details. Extensive simulation results illustrate that our proposed filter not only achieves these three objectives but also possesses robust and adaptive capabilities, and outperforms other proposed filtering techniques.

10

Application of the adaptive noise removal technique to the enhancement of cDNA microarray images

Smolka B., Bieda R.

Journal of Medical Informatics & Technologies

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2005

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Vol. 9

131--142

EN

In this paper a novel class of filters designed for the removal of impulsive noise in colour images is presented. The proposed filter family is based on the kernel function which controls the noise suppression properties of the new filtering scheme. The comparison of the new filtering method with the standard techniques used for impulsive noise removal indicates its superior noise removal capabilities and excellent structure preserving properties. The proposed filtering scheme has been successfully applied to the denoising of the cDNA microarray images. Experimental results proved that the new filter is capable of removing efficiently the impulses present in multichannel images, while preserving their textural features.

11

Nonparametric design of impulsive noise removal in colour images

Smolka B., Budzan S., Lukač R.

Journal of Medical Informatics & Technologies

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2004

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Vol. 7

MIP3--12

EN

In this paper the problem of nonparametric impulsive noise removal in multichannel images is addressed. The proposed filter class is based on the nonparametric estimation of the density probability function in a sliding filter window. The obtained results show good noise removal capabilities and excellent structure preserving properties of the new impulsive noise removal technique.

12

Quantitative infrared thermography and convective heat transfer measurements

Carlomagno G. M.

Machine Graphics and Vision

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1999

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Vol. 8, No. 4

509-528

EN

When using infrared thrmography to perform convective heat transfer measurements, it is necessery to restore the thermal images because of thier degradation which is due to the heat flux sensor, the environment and the temperature sensor. This problem is addressed herein. Besides, infrared thermography is employed to study three different fluid flow configurations ; in particular: the heat transfer to a jet centrally impinging on a rotating disk; the complex heat transfer pattern associated with a jet in cross-flow ; the heat transfer distribution along 180° turn channel. Attention is focused on the capability of the infrared thermography to deal with complex flow dynamics, the interaction between the jet and the boundary layer linked to the disk rotation, heat transfer developing in the wake region of a jet in cross-flow, high heat transfer regions and recirculation bubbles in a 180° turn channel.