Coding Artifacts Robust Resolution Up-conversion

Shao, Ling; Hu, Hao; Haan, G. de

doi:10.1109/icip.2007.4379852

Cited by 11 publications

(12 citation statements)

References 8 publications

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“…Recently, the advanced non-linear resolution up-scaling algorithm developed rapidly, such as Kondo's data dependent interpolation filter [7], CBA [8].…”

Section: Resolution Up-scalingmentioning

confidence: 99%

Repairing imperfect video enhancement algorithms using classification-based trained filters

Shao

Zhang

Wang

et al. 2011

SIViP

Self Cite

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Multifarious image enhancement algorithms have been used in different applications specifically. Still, some algorithm and modules are imperfect for practical use. When the image enhancement modules have been fixed or combined by a series of algorithm, we need to repair it as a whole part without changing the inside. This report aim to find an algorithm based on trained filters to repair low-quality image enhancement modules. A brief review on basic image enhancement techniques and pixel classification methods will be presented, and the procedure of trained filter will be described step by step. The experiments and results comparisons for this algorithm will be described in detail.

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“…Recently, the advanced non-linear resolution up-scaling algorithm developed rapidly, such as Kondo's data dependent interpolation filter [7], CBA [8].…”

Section: Resolution Up-scalingmentioning

confidence: 99%

Repairing imperfect video enhancement algorithms using classification-based trained filters

Shao

Zhang

Wang

et al. 2011

SIViP

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this case, we know all the entries of F. Equation (7) can be applied to each row and column of F . As an illustration, let us consider the following statement for the first row: C is the first row of matrix F , B is obtained using (9), and matrix A is unknown. As a result, we should use (7) eight times, four times for the rows, and four times for the columns of matrix F. In as much as (7), itself, contains n equations, where n is the size of vector C, we have 4 × 8 = 32 equations (where four is the number of equations:[ ).…”

Section: Two-dimensional Algorithmmentioning

confidence: 99%

“…In [10], the authors have extended the algorithm presented in [9] to display products such as video restoration.…”

Section: Introductionmentioning

confidence: 99%

“…In the last few decades, several authors have carried out researches on this topic. Along the same line, we briefly introduce [3][4][5][6][7][8][9][10] as follows.…”

Section: Introductionmentioning

confidence: 99%

“…In [9], in order to enlarge the image, it is downsampled by using the bilinear method and then, the missing pixels are estimated by using a combination of the other pixels, while the desired coefficients are optimized by using a least mean square technique. In [10], the authors have extended the algorithm presented in [9] to display products such as video restoration.…”

Section: Introductionmentioning

confidence: 99%

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A novel method in adaptive image enlargement

Bayat

Kafaie

Ayremlou

et al. 2012

EURASIP J. Adv. Signal Process.

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This article introduces a new adaptive method for image interpolation. In order to obtain a high resolution (HR) image from its low resolution (LR) counterpart (original image), an interpolator function (array) is used, and the main focus of this manuscript is to formulate and define this function. By applying this interpolator function to each row and column of a LR image, it is possible to construct its HR counterpart. One of the main challenges of image interpolation algorithms is to maintain the edge structures while developing an HR image from the LR replica. The proposed approach overcomes this challenge and exhibits remarkable results at the image edges. The peak signal to noise ratio and structural similarity criteria by using this innovative technique are notably better than those achieved by alternative schemes. Also, in terms of implementation speed, this method displays a clear advantage and outperforms the high performance algorithms in the ability to decrease the artifact results of image enlargement such as blurring and zigzagging.

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A multi-processor NoC-based architecture for real-time image/video enhancement

Saponara

Fanucci

Petri

2011

J Real-Time Image Proc

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The paper presents a multi-processor architecture for real-time and low-power image and video enhancement applications. Differently from other state-of-the-art parallel architectures the proposed solution is composed of heterogeneous tiles. The tiles have computational and memory capabilities, support different algorithmic classes and are connected by a novel Network-on-Chip (NoC) infrastructure. The proposed packet-switched data transfer scheme avoids communication bottlenecks when more tiles are working concurrently. The functional performances of the NoC-based multi-processor architecture are assessed by presenting the achieved results when the platform is programmed to support different enhancement algorithms for still images or videos. The implementation complexity of the NoC-based multi-tile platform, integrated in 65 nm CMOS technology, is reported and discusse

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Coding Artifacts Robust Resolution Up-conversion

Cited by 11 publications

References 8 publications

Repairing imperfect video enhancement algorithms using classification-based trained filters

Repairing imperfect video enhancement algorithms using classification-based trained filters

A novel method in adaptive image enlargement

A multi-processor NoC-based architecture for real-time image/video enhancement

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