Multi‐frame super‐resolution algorithm using common vector approach

Seke, Erol; Anagün, Yıldıray; Adar, Nihat

doi:10.1049/iet-ipr.2018.5168

Cited by 4 publications

(3 citation statements)

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“…To improve the resolution in the image area as well as in the text, video processing super‐resolution (SR)‐based algorithms works better and gets popularity among other techniques. For image reconstruction, many methods have been proposed in the research in which the SR algorithms play a vital role in improving the image resolutions as accurately as possible while preserving the essential data of the image scene [2, 3].…”

Section: Introductionmentioning

confidence: 99%

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Sparse representation based computed tomography images reconstruction by coupled dictionary learning algorithm

et al. 2020

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It is very interesting to reconstruct high‐resolution computed tomography (CT) medical images that are very useful for clinicians to analyse the diseases. This study proposes an improved super‐resolution method for CT medical images in the sparse representation domain with dictionary learning. The sparse coupled K‐singular value decomposition (KSVD) algorithm is employed for dictionary learning purposes. Images are divided into two sets of low resolution (LR) and high resolution (HR), to improve the quality of low‐resolution images, the authors prepare dictionaries over LR and HR image patches using the KSVD algorithm. The main idea behind the proposed method is that sparse coupled dictionaries learn about each patch and establish the relationship between sparse coefficients of LR and HR image patches to recover the HR image patch for LR image. The proposed method is compared to conventional algorithms in terms of mean peak signal‐to‐noise ratio and structural similarity index measurements by using three different data set images, including CT chest, CT dental and CT brain images. The authors also analysed the proposed improved method for different dictionary sizes and patch size to obtain a similar high‐resolution image. These parameters play an essential role in the reconstruction of the HR images.

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Section: Introductionmentioning

confidence: 99%

“…This pre‐learning was combined with the ability to map the intensity imbalance of sparse representation coefficients in different resolution levels. Seke et al [31] proposed an improved method of motion estimation using SR technology. To reconstruct the HR image, a common vector approach was used to detect motion.…”

Section: Introductionmentioning

confidence: 99%

Sparse representation based computed tomography images reconstruction by coupled dictionary learning algorithm

et al. 2020

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“…SISR deals with synthesis of an HR image from a single LR observation where

K = 1

in (1). MFSR uses multiple LR observations to synthesis the HR image [4]. Noise may be added to the image during the acquisition and/or further processing stages.…”

Section: Introductionmentioning

confidence: 99%

Multi‐frame image super resolution using spatially weighted total variation regularisations

Rahiman

George

2020

IET image process

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Multi-Frame Super Resolution with Deep Residual Learning on Flow Registered Non-Integer Pixel Images

Noor

Zhu

et al. 2019

2019 IEEE International Conference on Image Processing (ICIP)

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Multi‐frame super‐resolution algorithm using common vector approach

Cited by 4 publications

References 48 publications

Sparse representation based computed tomography images reconstruction by coupled dictionary learning algorithm

Sparse representation based computed tomography images reconstruction by coupled dictionary learning algorithm

Multi‐frame image super resolution using spatially weighted total variation regularisations

Multi-Frame Super Resolution with Deep Residual Learning on Flow Registered Non-Integer Pixel Images

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