Super-Resolution Based on Curvelet Transform and Sparse Representation

Ismail, Israa; Eltoukhy, Mohamed Meselhy; ElTaweel, Ghada

doi:10.32604/csse.2023.028906

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…These approaches are very easy to implement, but do not yield unique solutions due to the ill-posed nature of the inverse problem. Sparse Representation method proposed by Ismail et al [8] and Sparse-coding-based SR MRI algorithm suggested by Wang et al [9] to find the sparse representation through a dictionary learning process. This algorithm has a little bit of improvement in the MRI low-resolution image, but still, the result is not satisfactory due to the limited resolution improvement, and it turns very slowly for 3D MRI images.…”

Section: Introductionmentioning

confidence: 99%

IRMIRS: Inception-ResNet-Based Network for MRI Image Super-Resolution

Muhammad¹,

Bhutto²,

Masroor³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Medical image super-resolution is a fundamental challenge due to absorption and scattering in tissues. These challenges are increasing the interest in the quality of medical images. Recent research has proven that the rapid progress in convolutional neural networks (CNNs) has achieved superior performance in the area of medical image super-resolution. However, the traditional CNN approaches use interpolation techniques as a preprocessing stage to enlarge low-resolution magnetic resonance (MR) images, adding extra noise in the models and more memory consumption. Furthermore, conventional deep CNN approaches used layers in series-wise connection to create the deeper mode, because this later end layer cannot receive complete information and work as a dead layer. In this paper, we propose Inception-ResNet-based Network for MRI Image Super-Resolution known as IRMRIS. In our proposed approach, a bicubic interpolation is replaced with a deconvolution layer to learn the upsampling filters. Furthermore, a residual skip connection with the Inception block is used to reconstruct a high-resolution output image from a low-quality input image. Quantitative and qualitative evaluations of the proposed method are supported through extensive experiments in reconstructing sharper and clean texture details as compared to the state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

IRMIRS: Inception-ResNet-Based Network for MRI Image Super-Resolution

Muhammad¹,

Bhutto²,

Masroor³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

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A guided optimized recursive least square adaptive filtering based multi-variate dense fusion network model for image interpolation

Diana Earshia,

Sumathi

2023

SIViP

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A Distorted-Image Quality Assessment Algorithm Based on a Sparse Structure and Subjective Perception

Yang,

Liu,

et al. 2024

Mathematics

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Most image quality assessment (IQA) algorithms based on sparse representation primarily focus on amplitude information, often overlooking the structural composition of images. However, structural composition is closely linked to perceived image quality, a connection that existing methods do not adequately address. To fill this gap, this paper proposes a novel distorted-image quality assessment algorithm based on a sparse structure and subjective perception (IQA-SSSP). This algorithm evaluates the quality of distorted images by measuring the sparse structure similarity between a reference and distorted images. The proposed method has several advantages. First, the sparse structure algorithm operates with reduced computational complexity, leading to faster processing speeds, which makes it suitable for practical applications. Additionally, it efficiently handles large-scale data, further enhancing the assessment process. Experimental results validate the effectiveness of the algorithm, showing that it achieves a high correlation with human visual perception, as reflected in both objective and subjective evaluations. Specifically, the algorithm yielded a Pearson correlation coefficient of 0.929 and a mean squared error of 8.003, demonstrating its robustness and efficiency. By addressing the limitations of existing IQA methods and introducing a more holistic approach, this paper offers new perspectives on IQA. The proposed algorithm not only provides reliable quality assessment results but also closely aligns with human visual experience, thereby enhancing both the objectivity and accuracy of image quality evaluations. This research offers significant theoretical support for the advancement of sparse representation in IQA.

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Super-Resolution Based on Curvelet Transform and Sparse Representation

Cited by 3 publications

References 23 publications

IRMIRS: Inception-ResNet-Based Network for MRI Image Super-Resolution

IRMIRS: Inception-ResNet-Based Network for MRI Image Super-Resolution

A guided optimized recursive least square adaptive filtering based multi-variate dense fusion network model for image interpolation

A Distorted-Image Quality Assessment Algorithm Based on a Sparse Structure and Subjective Perception

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