Noise Robust Face Hallucination via Locality-Constrained Representation

Jiang, Junjun; Hu, Ruimin; Wang, Zhongyuan; Han, Zhen

doi:10.1109/tmm.2014.2311320

Cited by 210 publications

(96 citation statements)

References 29 publications

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“…We also test the performance of the entire face restoring method using some randomly selected face in a surveillance video. NE [4], LcR [7] methods are also tested for better comparison.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Research on Face Restoring in Surveillance Videos

He¹,

Wen²

2016

IJMUE

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“…We also test the performance of the entire face restoring method using some randomly selected face in a surveillance video. NE [4], LcR [7] methods are also tested for better comparison.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Research on Face Restoring in Surveillance Videos

He¹,

Wen²

2016

IJMUE

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“…Super resolution based SU is a recently developed spectral unmixing approach, in the future, we will consider how to apply the proposed SU model to the hyperspectral face image to super-resolve a high-resolution face [41].…”

Section: Discussionmentioning

confidence: 99%

Hyperspectral Unmixing with Robust Collaborative Sparse Regression

Mei

et al. 2016

Remote Sensing

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Abstract:Recently, sparse unmixing (SU) of hyperspectral data has received particular attention for analyzing remote sensing images. However, most SU methods are based on the commonly admitted linear mixing model (LMM), which ignores the possible nonlinear effects (i.e., nonlinearity). In this paper, we propose a new method named robust collaborative sparse regression (RCSR) based on the robust LMM (rLMM) for hyperspectral unmixing. The rLMM takes the nonlinearity into consideration, and the nonlinearity is merely treated as outlier, which has the underlying sparse property. The RCSR simultaneously takes the collaborative sparse property of the abundance and sparsely distributed additive property of the outlier into consideration, which can be formed as a robust joint sparse regression problem. The inexact augmented Lagrangian method (IALM) is used to optimize the proposed RCSR. The qualitative and quantitative experiments on synthetic datasets and real hyperspectral images demonstrate that the proposed RCSR is efficient for solving the hyperspectral SU problem compared with the other four state-of-the-art algorithms.

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“…Instead of selecting a fixed K number of neighbors, an adaptive selection of neighbors is proposed by Jiang et al [17]. To avoid the over-fitting or under-fitting condition that prevails, a locality adaptor is incorporated.…”

Section: B1 Methods Using Sparse Representationmentioning

confidence: 99%

Face Super Resolution: A Survey

Kanakaraj¹,

Govindan²,

Kalady³

2017

IJIGSP

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Abstract-Accurate recognition and tracking of human faces are indispensable in applications like Face Recognition, Forensics, etc. The need for enhancing the low resolution faces for such applications has gathered more attention in the past few years. To recognize the faces from the surveillance video footage, the images need to be in a significantly recognizable size. Image Super-Resolution (SR) algorithms aid in enlarging or super-resolving the captured low-resolution image into a high-resolution frame. It thereby improves the visual quality of the image for recognition. This paper discusses some of the recent methodologies in face super-resolution (FSR) along with an analysis of its performance on some benchmark databases. Learning based methods are by far the immensely used technique. Sparse representation techniques, Neighborhood-Embedding techniques, and Bayesian learning techniques are all different approaches to learning based methods. The review here demonstrates that, in general, learning based techniques provides better accuracy/ performance even though the computational requirements are high. It is observed that Neighbor Embedding provides better performances among the learning based techniques. The focus of future research on learning based techniques, such as Neighbor Embedding with Sparse representation techniques, may lead to approaches with reduced complexity and better performance.

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Noise Robust Face Hallucination via Locality-Constrained Representation

Cited by 210 publications

References 29 publications

Research on Face Restoring in Surveillance Videos

Research on Face Restoring in Surveillance Videos

Hyperspectral Unmixing with Robust Collaborative Sparse Regression

Face Super Resolution: A Survey

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