Sparse Support Regression for Image Super-Resolution

Jiang, Junjun; Ma, Xiang; Cai, Zhihua; Hu, Ruimin

doi:10.1109/jphot.2015.2484287

Cited by 37 publications

(13 citation statements)

References 28 publications

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“…10. For these test images, we performed SR by using the proposed methods and the previously reported methods in [61], [62] and [64]. These methods were selected since they are state-of-the-art methods 4 .…”

Section: B Results Of Super-resolutionmentioning

confidence: 99%

“…Compared to the problem of image inpainting, that of the SR is more difficult since the missing components tend to be larger. Therefore, the use of division into multiple sub-problems becomes important 4 Due to the limitation of space, we only performed a comparison between our method and state-of-the-art methods in [61], [62] and [64]. Examples of SR obtained by our method and the methods in [54], [57], [58], [60], [61], [62] and [64] are shown in the supplemental materials.…”

Section: B Results Of Super-resolutionmentioning

confidence: 99%

“…Zhou et al also proposed a single image SR method based on compact KPCA coding and kernel regression, and their method also includes a clustering-based scheme [62]. Sun et al [63] and Jiang et al [64] respectively proposed sparse representation-based SR methods, and the latter one can simultaneously consider the manifold geometrical structure of patch manifold space and the support of the corresponding sparse coefficients. Cao et al proposed an SR method based on image interpolation via low-rank matrix completion and recovery [65].…”

Section: ) Super-resolutionmentioning

confidence: 99%

“…In this experiment, we reduced the size of the nine images in Fig. 10 to a quarter size and restored their original HR images by the proposed methods, the previously reported methods in [54], [57], [58], [60], [61], [62] and [64] and the comparative methods shown in Table I. Table II shows results for the SSIM index obtained from the estimated HR images.…”

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confidence: 99%

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Adaptive Subspace-Based Inverse Projections via Division Into Multiple Sub-Problems for Missing Image Data Restoration

Ogawa

Haseyama

2016

IEEE Trans. on Image Process.

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Abstract-This paper presents adaptive subspace-based inverse projections via division into multiple sub-problems (ASIP-DIMS) for missing image data restoration. In the proposed method, a target problem for estimating missing image data is divided into multiple sub-problems, and each sub-problem is iteratively solved with constraints of other known image data. By projection into a subspace model of image patches, the solution of each subproblem is calculated, where we call this procedure "subspacebased inverse projection" for simplicity. The proposed method can use higher-dimensional subspaces for finding unique solutions in each sub-problem, and successful restoration becomes feasible since a high level of image representation performance can be preserved. This is the biggest contribution of this paper. Furthermore, the proposed method generates several subspaces from known training examples and enables derivation of a new criterion in the above framework to adaptively select the optimal subspace for each target patch. In this way, the proposed method realizes missing image data restoration using ASIP-DIMS. Since our method can estimate any kind of missing image data, its potential in two image restoration tasks, image inpainting and super-resolution, based on several methods for multivariate analysis is also shown in this paper.

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Section: B Results Of Super-resolutionmentioning

confidence: 99%

Section: B Results Of Super-resolutionmentioning

confidence: 99%

Section: ) Super-resolutionmentioning

confidence: 99%

mentioning

confidence: 99%

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Adaptive Subspace-Based Inverse Projections via Division Into Multiple Sub-Problems for Missing Image Data Restoration

Ogawa

Haseyama

2016

IEEE Trans. on Image Process.

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“…A mapping function from Low Resolution (LR) patches to High resolution (HR) patches is learnt by a local regression algorithm called sparse support regression, which can be constructed from the support basis of LR-HR dictionary. Also to preserve the geometrical structure of image patch dictionary, which is critical for reducing artifacts and obtain better visual quality [4]. Images of the rare end of car are considered.…”

Section: Literature Surveymentioning

confidence: 99%

Smart Solution to Towing Vehicles System

Jaju¹,

Batole²,

Dhiwar³

et al. 2016

IJCA

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It is necessary to digitalize the Government for making India corruption free with the help of information and communication technology (ICT) along with internet to achieve better government system by providing public services and processing internal works in suitable, cost effective and customer leaning manner. E-police system is also an egovernment related services which makes the communication process between public and police more efficient. Though epolice system is not a new thing in developed countries, but it is new for developing countries like India. Our system will definitely help to solve some issues which are currently faced by vehicle owners and towing agents.

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Face Image Super-Resolution via Sparse Representation and Local Texture Constraints

Wei

2019

Intelligent Computing Theories and Application

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Sparse Support Regression for Image Super-Resolution

Cited by 37 publications

References 28 publications

Adaptive Subspace-Based Inverse Projections via Division Into Multiple Sub-Problems for Missing Image Data Restoration

Adaptive Subspace-Based Inverse Projections via Division Into Multiple Sub-Problems for Missing Image Data Restoration

Smart Solution to Towing Vehicles System

Face Image Super-Resolution via Sparse Representation and Local Texture Constraints

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