Single image super-resolution by approximated Heaviside functions

Deng, Liang-Jian; Guo, Weihong; Huang, Ting‐Zhu

doi:10.1016/j.ins.2016.02.015

Cited by 37 publications

(30 citation statements)

References 63 publications

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“…ScSR [20] is not compared here because it requires HR images for training. AHF [4] approach is not sparse coding method and needs Table 1. The PSNR (dB) results obtained through the experiments, HFSR is the proposed method with conventional refinement, while HFSR(multi-scale) is the proposed method with multi-scale Refinement.…”

Section: Resultsmentioning

confidence: 99%

“…However, SR is an ill-posed issue which is very difficult to obtain the optimal solution. The approaches for SISR can be divided into three categories including interpolation-based methods [22,4,11,16,3], reconstruction-based methods [2,18,6,7,9], and learning-based methods [10,12,20,5,17,15]. Reconstruction-based methods and learning-based methods often yield more accurate results than interpolation-based algorithm, since those methods can acquire more information from statistical prior and external data even though more time-consuming.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Function Sparse Representation Towards Image Super Resolution

Bian

Lin

Zhang

2019

Computer Analysis of Images and Patterns

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Sparse representation with training-based dictionary has been shown successful on super resolution(SR) but still have some limitations. Based on the idea of making the magnification of function curve without losing its fidelity, we proposed a function based dictionary on sparse representation for super resolution, called hybrid function sparse representation (HFSR). The dictionary we designed is directly generated by preset hybrid functions without additional training, which can be scaled to any size as is required due to its scalable property. We mixed approximated Heaviside function (AHF), sine function and DCT function as the dictionary. Multi-scale refinement is then proposed to utilize the scalable property of the dictionary to improve the results. In addition, a reconstruct strategy is adopted to deal with the overlaps. The experiments on Set14 SR dataset show that our method has an excellent performance particularly with regards to images containing rich details and contexts compared with non-learning based state-of-the art methods.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Function Sparse Representation Towards Image Super Resolution

Bian

Lin

Zhang

2019

Computer Analysis of Images and Patterns

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“…We can distinguish between Multiple-Image Super-resolution (MISR) and Single Image Superresolution (SISR). SISR [30] allows to obtain a HR image from only one observed LR image by applying, for instance, interpolation [31][32][33][34] or machine learning techniques based on LR/HR image patches [35][36][37][38][39], see [40] for the use of deep learning techniques in image recovery problems. However, when a video sequence or a set of LR images is available, MISR is preferred.…”

Section: State-of-the-artmentioning

confidence: 99%

Image super-resolution for outdoor digital forensics. Usability and legal aspects

Villena

Vega

Mateos

et al. 2018

Computers in Industry

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Digital Forensics encompasses the recovery and investigation of data, images, and recordings found in digital devices in order to provide evidence in the court of law. This paper is devoted to the assessment of digital evidence which requires not only an understanding of the scientific technique that leads to improved quality of surveillance video recordings, but also of the legal principles behind it. Emphasis is given on the special treatment of image processing in terms of its handling and explanation that would be acceptable in a court of law. In this context, we propose a variational Bayesian approach to multiple-image super-resolution based on Super-Gaussian prior models that automatically enhances the quality of outdoor video recordings and estimates all the model parameters while preserving the authenticity, credibility and reliability of video data as digital evidence. The proposed methodology is validated both quantitatively and visually on synthetic videos generated from single images and real-life videos and applied to a real-life case of damages and stealing in a private property.

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“…So it is more intuitive to consider the correlation of different patches. From another aspect, patch is successfully used in the field of image processing recently, not only face recognition [14–16] but also image denoising [17–19], image superresolution [20, 21], and image decomposition (cartoon-texture [22, 23] or illumination-reflectance [24] and further retinex image enhancement [25]). Patch is becoming a basic tool in these above-mentioned literatures.…”

Section: Introductionmentioning

confidence: 99%

Patch-Based Principal Component Analysis for Face Recognition

Jiang

Huang

Zhao

et al. 2017

Computational Intelligence and Neuroscience

Self Cite

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We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter. By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction. Finally, we use the nearest neighbor classifier. Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA. Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.

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Single image super-resolution by approximated Heaviside functions

Cited by 37 publications

References 63 publications

Hybrid Function Sparse Representation Towards Image Super Resolution

Hybrid Function Sparse Representation Towards Image Super Resolution

Image super-resolution for outdoor digital forensics. Usability and legal aspects

Patch-Based Principal Component Analysis for Face Recognition

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