Block-Based MAP Superresolution Using Feature-Driven Prior Model

Xu, Feng; Fan, Tanghuai; Huang, Chenrong; Wang, Xin; Xu, Lizhong

doi:10.1155/2014/508357

Cited by 4 publications

(4 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When 𝐷 and 𝐻 represent downsampling and blurring, respectively, the restoration process becomes single image superresolution. The HR image 𝑋 and motion blur kernel 𝐻 are given by { X, H} = arg min{‖𝐷𝐻𝑋 − 𝑌‖ 2 2 }. The uniform downsampling operator 𝐷 is assumed to be known in advance.…”

Section: The Proposed Frameworkmentioning

confidence: 99%

“…Image superresolution is a technique designed to improve the resolution of images obtained by LR sensors avoiding blurring and artifacts. This technique is generally employed to exceed the inherent limitations of LR imaging (e.g., mobile smartphone or portable digital cameras) and make better use of the growing capability of HR displays (e.g., high-definition television (HDTV)), which are relatively inexpensive to complement [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Joint Motion Deblurring and Superresolution from Single Blurry Image

Liu

2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Currently superresolution from a motion blurred image still remains a challenging task. The conventional approach, which preprocesses the blurry low resolution (LR) image with a deblurring algorithm and employs a superresolution algorithm, has the following limitation. The high frequency texture of the image is unavoidably lost in the deblurring process and this loss restricts the performance of the subsequent superresolution process. This paper presents a novel technique that performs motion deblurring and superresolution jointly from one single blurry image. The basic idea is to regularize the ill-posed reconstruction problem using an edge-preserving gradient prior and a sparse kernel prior. This method derives from an inverse problem approach under an efficient optimization scheme that alternates between blur kernel estimation and superresolving until convergence. Furthermore, this paper proposes a simple and efficient refinement formulation to remove artifacts and render better deblurred high resolution (HR) images. The improvements brought by the proposed combined framework are demonstrated by the processing results of both simulated and real-life images. Quantitative and qualitative results on challenging examples show that the proposed method outperforms the existing state-of-the-art methods and effectively eliminates motion blur and artifacts in the superresolved image.

show abstract

Section: The Proposed Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joint Motion Deblurring and Superresolution from Single Blurry Image

Liu

2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…bilateral filtering, feature driven methods, locality constraints, etc.) can improve the classification precision for a specific parameter to within a certain precision [1][2][3]. However, over the past decade, several novel methods have been proposed which are uniquely suited for specific types of classification.…”

Section: Introductionmentioning

confidence: 99%

Nearest-Neighbors Based Weighted Method for the BOVW Applied to Image Classification

Xu¹,

Sun²,

Lu³

et al. 2015

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

-This paper presents a new Nearest-Neighbors based weighted representation for images and weighted K-Nearest-Neighbors (WKNN) classifier to improve the precision of image classification using the Bag of Visual Words (BOVW) based models. Scale-invariant feature transform (SIFT) features are firstly extracted from images. Then, the K-means++ algorithm is adopted in place of the conventional K-means algorithm to generate a more effective visual dictionary. Furthermore, the histogram of visual words becomes more expressive by utilizing the proposed weighted vector quantization (WVQ). Finally, WKNN classifier is applied to enhance the properties of the classification task between images in which similar levels of background noise are present. Average precision and absolute change degree are calculated to assess the classification performance and the stability of K-means++ algorithm, respectively. Experimental results on three diverse datasets: Caltech-101, Caltech-256 and PASCAL VOC 2011 show that the proposed WVQ method and WKNN method further improve the performance of classification.

show abstract

“…With the rapid development of SRR, it has been widely applied in many fields such as medical imaging, remote sensing imaging, military surveillance, and image compression [3]- [7]. Generally, the SRR methods can be classified into three categories: interpolation based methods [8] [9], reconstruction based methods [10] [11], and learning based methods [12] [13]. This paper will mainly discuss the SRR based on sparse representation (SR) method of the learning based methods [14] [15].…”

Section: Introductionmentioning

confidence: 99%

One new method on image super-resolution reconstruction

Zhang

Wang

et al. 2015

2015 IEEE International Conference on Information and Automation

Self Cite

View full text Add to dashboard Cite

According to the image quality degradation in super-resolution reconstruction, we present a new algorithm for a single image super-resolution reconstruction to improve the image resolution. Considering the limitations that the extracted features of low-resolution image can not adapt to image direction changes, we propose a new feature-extracted method, and we build a new global optimization framework with analysis sparse prior based on the multiple extracted features, joint dictionary learning. Experiments show the effectiveness of our method. Index Terms -resolution reconstruction, sparse representation, multi-features extraction, sparse prior..

show abstract

Block-Based MAP Superresolution Using Feature-Driven Prior Model

Cited by 4 publications

References 23 publications

Joint Motion Deblurring and Superresolution from Single Blurry Image

Joint Motion Deblurring and Superresolution from Single Blurry Image

Nearest-Neighbors Based Weighted Method for the BOVW Applied to Image Classification

One new method on image super-resolution reconstruction

Contact Info

Product

Resources

About