A new multiframe super-resolution based on nonlinear registration and a spatially weighted regularization

Laghrib, Amine; Hadri, Aissam; Hakim, Abdelilah; Raghay, Said

doi:10.1016/j.ins.2019.04.029

Cited by 25 publications

(19 citation statements)

References 39 publications

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“…Lastly, the additive system noise is indicated by n k . Accordingly, the image degradation model which is used to simulate the whole process can be rewritten as in the following equation [8,9,11,13]:…”

Section: Image Observation Modelmentioning

confidence: 99%

“…where D and H describe the same motion and blur matrices, respectively. e image degradation model is demonstrated in Figure 1 [11,32].…”

Section: Image Observation Modelmentioning

confidence: 99%

“…In many real-life imaging systems, there are a variety of possible factors for reducing the image resolution due to various physical constraints, inadequate photo detectors, a lower rate of spatial sampling, and an inefficient method for capturing images [8][9][10][11]. In the past years, super-resolution (SR) image reconstruction approaches have become a powerful and cost-effective solution to increase the quality of the recorded LR images, meeting the growing market demand for HR images [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…e main aim of the regularization method is to resolve the SR issue due to its ill-conditional nature. Recent works have been conducted aiming at resolving the SR issue by exploiting the regularization framework [8][9][10][11][19][20][21][22][23][24][25][26]. e idea of reconstruction of the SR image relies on the regularization process that generates the HR image through attempting to minimize the main objective function, where it incorporates both the fidelity of data and the regularization terms.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear Filters

Khattab

Zeki

Alwan

et al. 2021

Journal of Electrical and Computer Engineering

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The primary goal of the multiframe super-resolution image reconstruction is to produce an image with a higher resolution by integrating information extracted from a set of corresponding images with low resolution, which is used in various fields. However, super-resolution image reconstruction approaches are typically affected by annoying restorative artifacts, including blurring, noise, and staircasing effect. Accordingly, it is always difficult to balance between smoothness and edge preservation. In this paper, we intend to enhance the efficiency of multiframe super-resolution image reconstruction in order to optimize both analysis and human interpretation processes by improving the pictorial information and enhancing the automatic machine perception. As a result, we propose new approaches that firstly rely on estimating the initial high-resolution image through preprocessing of the reference low-resolution image based on median, mean, Lucy-Richardson, and Wiener filters. This preprocessing stage is used to overcome the degradation present in the reference low-resolution image, which is a suitable kernel for producing the initial high-resolution image to be used in the reconstruction phase of the final image. Then, L2 norm is employed for the data-fidelity term to minimize the residual among the predicted high-resolution image and the observed low-resolution images. Finally, bilateral total variation prior model is utilized to restrict the minimization function to a stable state of the generated HR image. The experimental results of the synthetic data indicate that the proposed approaches have enhanced efficiency visually and quantitatively compared to other existing approaches.

show abstract

Section: Image Observation Modelmentioning

confidence: 99%

“…where D and H describe the same motion and blur matrices, respectively. e image degradation model is demonstrated in Figure 1 [11,32].…”

Section: Image Observation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear Filters

Khattab

Zeki

Alwan

et al. 2021

Journal of Electrical and Computer Engineering

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show abstract

“…In [30], an affine SIFT-based local registration method was proposed by Wang et al for remote sensing images, where a framework for localization in the image registration was developed and the local registration accuracy was strengthened. Furthermore, Laghrib et al [31] proposed a spatially weighted second-order super-resolution method, where all the LR images are registered by a hyperelastic registration method, which is used to handle the subpixel errors between the unregistered images.…”

mentioning

confidence: 99%

Unified Framework for the Joint Super-Resolution and Registration of Multiangle Multi/Hyperspectral Remote Sensing Images

Chen

Zhang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this article, a unified framework based on rank minimization (UFRM) is proposed for use with multiangle multi/hyperspectral remote sensing images, which simultaneously integrates image super-resolution reconstruction (SRR) and image registration. With the complementary information of different angle images and the high correlation between each band of the multi/hyperspectral images, a new image observation model is established to describe the mathematical degradation process of the observed low-resolution multiangle multi/hyperspectral images from the desired high-resolution (HR) multi/hyperspectral image. Based on the rank-one structure of the multiangle images, each observed image is decomposed into a foreground image for each angle image, and a background image, which is shared among all the multiangle images. A multichannel total variation constraint is applied to the target HR background image, with the consideration of the high correlation of different bands. Finally, an alternating minimization optimization strategy is utilized to resolve the joint cost function, which consists of the unknown image registration transformation parameters and the desired reconstruction image. As a result, the UFRM method can simultaneously achieve image registration and SRR. A number of experiments were conducted, which confirmed the superior performance of the proposed method.

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Computational imaging in multirotation cylinder lens based on precise angle estimation with principal component analysis

et al. 2021

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A new multiframe super-resolution based on nonlinear registration and a spatially weighted regularization

Cited by 25 publications

References 39 publications

Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear Filters

Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear Filters

Unified Framework for the Joint Super-Resolution and Registration of Multiangle Multi/Hyperspectral Remote Sensing Images

Computational imaging in multirotation cylinder lens based on precise angle estimation with principal component analysis

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