Image Super-Resolution Via Sparse Representation

Yang, Jianchao; Wright, John; Huang, Thomas S.; Ma, Yi

doi:10.1109/tip.2010.2050625

Cited by 4,415 publications

(656 citation statements)

References 25 publications

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“…It was also compared with promising reconstruction methods that employ wavelet transform (WT), such as Wavelet Zero Padding (WZP) [31] and Demirel-Anbarjafari Super Resolution (DASR) [12]. Finally, the proposed SR-WAFE-SR technique was compared with state-of-the-art learning methods that use sparse representation and CNN such as Super-Resolution with Sparse Mixing Estimators (SME) of Mallat et al [15], the Sparse coding of Yang et al (ScSR) [14], Beta Process Join Dictionary Learning (BP-JDL) of He [16], and the Super-Resolution Convolutional Neural Network (SRCNN) of Dong et al [20].…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…69). These images were obtained from the code provided by Yang et al [14] and Zeyde et al [23]. In this database, all the images are treated as HR images and the LR version is obtained by using the down-sampling operator and the blurring filter as follows:…”

Section: Dictionary Trainingmentioning

confidence: 99%

“…In the stateof-the-art methods, there are pioneering works that use sparse representation to solve the inverse problem presented in (1). For example, Yang et al [14] proposed an SR technique, in which the LR image is decomposed into patches where for every patch, the sparse representation is performed, and then their coefficients are used to recover the HR image. This sparse representation improves a pair of dictionaries that have the similarity of sparse representations among the HR and the LR patches.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Image Super-Resolution via Wavelet Feature Extraction and Sparse Representation

Alvarez-Ramos¹,

Ponomaryov²,

Sadovnychiy³

2018

RADIOENGINEERING

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show abstract

Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Dictionary Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Image Super-Resolution via Wavelet Feature Extraction and Sparse Representation

Alvarez-Ramos¹,

Ponomaryov²,

Sadovnychiy³

2018

RADIOENGINEERING

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show abstract

“…However, as SAR images contain rich feature information, an ideal de-noising result can hardly be realized if only by limited orthogonal transform, which cannot represent all the image features. The recent development of sparse representation has led to its widespread use in image processing, such as super-resolution reconstruction [11], edge detection [12], and face recognition [13]. The image de-noising method based on sparse representation of dictionary learning is applied to suppress the speckle of SAR images in [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

SAR Image De-Noising Based on Shift Invariant K-SVD and Guided Filter

Liu

2017

Remote Sensing

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Finding a way to effectively suppress speckle in SAR images has great significance. K-means singular value decomposition (K-SVD) has shown great potential in SAR image de-noising. However, the traditional K-SVD is sensitive to the position and phase of the characteristics in the image, and the de-noised image by K-SVD has lost some detailed information of the original image. In this paper, we present one new SAR image de-noising method based on shift invariant K-SVD and guided filter. The whole method consists of two steps. The first deals mainly with the noisy image with shift invariant K-SVD and obtaining the initial de-noised image. In the second step, we do the guided filtering for the initial de-noised image. Finally, we can recover the final de-noised image. Experimental results show that our method not only has better visual effects and objective evaluation, but can also save more detailed information such as image edge and texture when de-noising SAR images. The presented shift invariant K-SVD can be widely used in image processing, such as image fusion, edge detection and super-resolution reconstruction.

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“…The key idea of these methods is to utilize the relationship between the high-resolution (HR) and the LR images, learned via training images, to help recover details in the target LR images. Freeman et al [2], Chang et al [3], and Yang et al [4] showed that state-of-the-art performance can be achieved by using various learning-based methods. The major limitation of these methods is that a significant amount of HR images are required for learning.…”

Section: Introductionmentioning

confidence: 99%

Non-local Means Resolution Enhancement of Lung 4D-CT Data

Zhang

Yap

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Abstract. Image resolution in 4D-CT is a crucial bottleneck that needs to be overcome for improved dose planning in radiotherapy for lung cancer. In this paper, we propose a novel patch-based algorithm to enhance the image quality of 4D-CT data. Our premise is that anatomical information missing in one phase can be recovered from complementary information embedded in other phases. We employ a patch-based mechanism to propagate information across phases for reconstruction of intermediate slices in the axial direction, where resolution is normally the lowest. Specifically, structurally-matching and spatially-nearby patches are combined for reconstruction of each patch. For greater sensitivity to anatomical nuances, we further employ a quad-tree technique to adaptively partition each slice of the image in each phase for more fine-grained refinement. Our evaluation based on a public 4D-CT lung data indicates that our algorithm gives very promising results with significantly enhanced image structures. Introduction4D-CT is becoming increasingly popular in lung cancer treatment for providing respiratory-related information that is essential for guiding radiation therapy effectively. However, due to the risk of radiation [1], only a limited number of CT segments are usually acquired, which often results in very low resolution along the inferior-superior direction. This low-resolution (LR) data are usually plagued with visible imaging artifacts such as vessel discontinuity and partial volume effect. More importantly, insufficient resolution further distorts the shape of a tumor. This distortion might finally interfere with optimal dose planning. Super-resolution (SR) reconstruction is an effective approach for improving image resolution. Classical SR methods can be divided into two major categories: interpolation-based and model-based. The main advantage of interpolation-based methods is their simplicity. However, blurred edges and undesirable artifacts are inevitable. Currently, more attention has been directed to model-based SR approach. The general assumption of model-based SR approaches is that the LR image is a degraded version

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Image Super-Resolution Via Sparse Representation

Cited by 4,415 publications

References 25 publications

Image Super-Resolution via Wavelet Feature Extraction and Sparse Representation

Image Super-Resolution via Wavelet Feature Extraction and Sparse Representation

SAR Image De-Noising Based on Shift Invariant K-SVD and Guided Filter

Non-local Means Resolution Enhancement of Lung 4D-CT Data

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