General observation model for an iterative multiframe regularized Super-Resolution Reconstruction for video enhancement

Patanavijit, Vorapoj; Jitapunkul, S.

doi:10.1109/ispacs.2009.4806676

Cited by 3 publications

(16 citation statements)

References 18 publications

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“…) stands for the proposed nonisometric inter-frame warp [5] between the images X and k Y . The relation between overlapping blocked HR Image and overlapping blocked LR image sequence is shown in Fig 1(c).…”

Section: B General Observation Model (Gom) [5]mentioning

confidence: 99%

“…This section briefly review a scheme for estimating affine blockbased motion vectors ( k F ) for registration step [5]. The estimation can be separated into 2 stages.…”

Section: The Registration For General Observation Model Of Srrmentioning

confidence: 99%

“…The criterion for parameter selection in this paper was based on experiments and the chosen parameters produce the highest PSNR result on 3 standard sequences: Foreman, Carphone and Stefan. From [5], the total number of the M3SS check points is fixed at 3.65E+3. Compared with the classical block-based estimation method (translation block-based estimation method) at 0.25 pixel accuracy and w=9, the total number of the M3SS check points has approximately 3 times more than the FS approach of the classical observation model but the PSNR performance of the M3SS is 5-6 dB higher than that of the classical translational method.…”

Section: The Registration For General Observation Model Of Srrmentioning

confidence: 99%

“…In this paper, we proposed the alternative video enhancement framework using a robust iterative SRR based on a Leclerc Bayesian approach with a general observation model [5]. Leclerc norm [6,7] is used for measuring the error between the projected estimate of the high quality reconstructed image and each low quality corrupted image and for removing outliers in the data.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, Tikhonov regularization is incorporated in the proposed framework in order to remove artifacts from the final answer and to improve the rate of convergence. In order to cope with real video sequences and complex motion sequences such as Susie and Foreman sequence, the proposed SRR algorithm is based on a GOM (General Observation Model or fast affine block-based transform [5]), devoted to the case of nonisometric inter-frame motion. These experimental results demonstrate that our method's performance is superior to what were proposed earlier [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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A Leclerc Bayesian approach for video reconstruction based on a robust iterative SRR and a General Observation Model

Patanavijit

2010

2010 10th International Symposium on Communications and Information Technologies

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Traditional Super-Resolution Reconstruction (SRR) vigorously falls back on the availability of accurate registration for this fusion task and the observation noise model. When the motion is registered inaccurately, as often happens for nonglobal motion fields, annoying artifacts appear in the superresolved outcome and when the observation noise is not AWGN, severe artifacts appear in the reconstructed result. This paper proposes the alternative robust SRR algorithm that can be successively applied on the real or standard sequence and can be applied on the sequences that are corrupted by various noise models. First, the proposed SRR algorithm is based on Bayesian framework with the Leclerc norm for measuring the error between the projected estimate of the high quality reconstructed image and each corrupted images and for removing outliers in the data. Second, the proposed algorithm is used a General Observation Model or GOM (or fast affine block-based transform) in order to cope with real complex motion or nonisometric inter-frame motion sequences. The experimental results demonstrate that the proposed algorithm can be well applied on real sequences such as Suzie and Foreman sequence at several noise models (such as AWGN, Poisson, Salt & Pepper noise and Speckle) and several noise power.

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Section: B General Observation Model (Gom) [5]mentioning

confidence: 99%

“…This section briefly review a scheme for estimating affine blockbased motion vectors ( k F ) for registration step [5]. The estimation can be separated into 2 stages.…”

Section: The Registration For General Observation Model Of Srrmentioning

confidence: 99%

Section: The Registration For General Observation Model Of Srrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Leclerc Bayesian approach for video reconstruction based on a robust iterative SRR and a General Observation Model

Patanavijit

2010

2010 10th International Symposium on Communications and Information Technologies

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An alternative SR spatial enhancement based on adaptive meridian filter and GOM registration for severe noisy blurred videos

Thakulsukanant

Patanavijit

2015

2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technolo

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Video Enhancement Based on A Robust Hampel Iterative SRR with a General Observation Model

2009

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This paper proposes a novel robust Super Resolution Reconstruction (SRR) framework that can enhance a real complex video sequence and is applicable to any noise models. Although SRR algorithms have received considerable attention within the traditional research community, these algorithms are typically very sensitive to their assumed model of data and noise, which limits their utility. The real noise models that corrupt the measured sequence are unknown; consequently, SRR algorithms using L1 or L2 norm may degrade the image sequence rather than enhance it therefore the robust norm applicable to several noise and data models is desired in SRR algorithms. This paper proposes a SRR framework based on the stochastic regularization technique of Bayesian MAP estimation by minimizing a cost function. In order to tolerate to any noise models, the Hampel norm is used for measuring the difference between the projected estimate of the high-resolution image and each low resolution image, and removing outliers in the data. Tikhonov regularization is used to remove artifacts from the final result and improve the rate of convergence. Moreover, in order to cope with real video sequences and complex motion sequences, this paper proposes a SRR General Observation Model (GOM or a±ne block-based transform) devoted to the case of nonisometric inter-frame motion. In the experimental section, the proposed framework can enhance real complex motion sequences, such as Suzie and Foreman sequence, and con¯rm the effectiveness of our algorithm and demonstrate its superiority to other SRR algorithms based on L1 and L2 norm for several noise models (such as AWGN, Poisson noise, Salt & Pepper noise and Speckle noise) at several noise power.

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General observation model for an iterative multiframe regularized Super-Resolution Reconstruction for video enhancement

Cited by 3 publications

References 18 publications

A Leclerc Bayesian approach for video reconstruction based on a robust iterative SRR and a General Observation Model

A Leclerc Bayesian approach for video reconstruction based on a robust iterative SRR and a General Observation Model

An alternative SR spatial enhancement based on adaptive meridian filter and GOM registration for severe noisy blurred videos

Video Enhancement Based on A Robust Hampel Iterative SRR with a General Observation Model

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