Adaptive saliency-based compressive sensing image reconstruction

Akbari, Ali; Mandache, Diana; Trocan, Maria; Granado, Bernard

doi:10.1109/icmew.2016.7574688

Cited by 18 publications

(11 citation statements)

References 11 publications

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“…On the other hand, there are many repetitive patterns and regular structures throughout the natural images. This NS model in combination with the LR prior existing in the natural images can be used as a regularization term to regularize the solution space of the minimization problem (8) and develop a much more accurate sparse representation model. The NS and LR models have been used in many applications, such as image compression [49], [50] and inverse problems [19]- [23], [51].…”

Section: Jsr-based Ec With Non-local and Localmentioning

confidence: 99%

“…This regularization term is incorporated into Eq. (8) to develop a more effective EC algorithm, called JSR-based EC with the NS and LR models (JSR+NL).…”

Section: Jsr-based Ec With Non-local and Localmentioning

confidence: 99%

“…Sparse representation (SR) has been a powerful tool for image processing compression and solving inverse problems over the past decade [5]- [8]. In this technique, a signal can be represented by linear combination of a few atoms chosen from a pool called dictionary [9].…”

Section: Introductionmentioning

confidence: 99%

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Joint Sparse Learning With Nonlocal and Local Image Priors for Image Error Concealment

Akbari

Trocan

Sanei

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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Joint sparse representation (JSR) model has recently emerged as a powerful technique with wide variety of applications. In this paper, the JSR model is extended to error concealment (EC) application, being effective to recover the original image from its corrupted version. This model is based on jointly learning a dictionary pair and two mapping matrices that are trained offline from external training images. Given the trained dictionaries and mappings, the restoration is done by transferring the recovery problem into the sparse representation domain with respect to the trained dictionaries, which is further transformed into a common space using the respective mapping matrices. Then, the reconstructed image is obtained by back projection into the spatial domain. In order to improve the accuracy and stability of the proposed JSR-based EC algorithm and avoid unexpected artifacts, the local and non-local priors are seamlessly integrated into the JSR model. The non-local prior is based on the self-similarity within natural images and helps to find an accurate sparse representation by taking a weighted average of similar areas throughout the image. The local prior is based on learning the local structural regularity of the natural images and helps to regularize the sparse representation, exploiting the strong correlation in the small local areas within the image. Compared with the state-of-the-art EC algorithms, the results show that the proposed method has better reconstruction performance in terms of objective and subjective evaluations.

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Section: Jsr-based Ec With Non-local and Localmentioning

confidence: 99%

“…This regularization term is incorporated into Eq. (8) to develop a more effective EC algorithm, called JSR-based EC with the NS and LR models (JSR+NL).…”

Section: Jsr-based Ec With Non-local and Localmentioning

confidence: 99%

See 1 more Smart Citation

Joint Sparse Learning With Nonlocal and Local Image Priors for Image Error Concealment

Akbari

Trocan

Sanei

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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“…3.1 Dictionary learning-based image codec Figure 1 presents the block diagram of the dictionary learning-based image coding (DLC) framework [7]. The DLC mainly has four main parts, pre-processing, dictionary learning, adaptive sparse representation and entropy coding.…”

Section: Image Compressionmentioning

confidence: 99%

“…The analysis and synthesis sparse signal modelling has led to the design effective algorithms for many image-processing applications, such as compression [2][3][4][5][6][7][8] and solving inverse problems [9][10][11][12][13][14][15][16][17][18][19][20][21][22]. A straightforward application of the sparse signal modelling in the field of image processing has been image compression due to providing a compact representation of the signal.…”

Section: Introductionmentioning

confidence: 99%

On the Application of Dictionary Learning to Image Compression

Akbari¹,

Trocan²

2020

Coding Theory

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Signal models are a cornerstone of contemporary signal and image-processing methodology. In this chapter, a particular signal modelling method, called synthesis sparse representation, is studied which has been proven to be effective for many signals, such as natural images, and successfully used in a wide range of applications. In this kind of signal modelling, the signal is represented with respect to dictionary. The dictionary choice plays an important role on the success of the entire model. One main discipline of dictionary designing is based on a machine learning methodology which provides a simple and expressive structure for designing adaptable and efficient dictionaries. This chapter focuses on direct application of the sparse representation, i.e. image compression. Two image codec based on adaptive sparse representation over a trained dictionary are introduced. Experimental results show that the presented methods outperform the existing image coding standards, such as JPEG and JPEG2000.

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