Lapped nonlinear interpolative vector quantization and image super-resolution

Sheppard, D.G.; Panchapakesan, K.; Bilgin, Ali; Hunt, B. R.; Marcellin, Michael W.

doi:10.1109/acssc.1997.680167

Cited by 3 publications

(4 citation statements)

References 7 publications

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“…To reduce the blocking artifacts with a less computational load, IVQ was proposed [17]. In general, IVQ maps the low-dimensional representative vector X into a high-dimensional representative vector Y, which is more efficient, in terms of the hardware complexity or the amount of data to be transmitted, than the method that would require a larger codebook [18]. IVQ requires two types of codebooks: LR codebook C and HR codebook C * , with the same codebook size N but different dimensions.…”

Section: Ivqmentioning

confidence: 99%

Image interpolation using interpolative classified vector quantization

Hong

Park

Yang

et al. 2008

Image and Vision Computing

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Section: Ivqmentioning

confidence: 99%

Image interpolation using interpolative classified vector quantization

Hong

Park

Yang

et al. 2008

Image and Vision Computing

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“…The dimensionality of the resulting vectors is expected to be much smaller than that of the original ones since many of the block images are correlated. This data compression approach is aimed at the same objective as the encoding of the DCT coefficients in the restoration approach developed by Sheppard et al [6], [7], and [10].…”

Section: B Dimensionality Reduction By Pcamentioning

confidence: 99%

“…In this paper, a different approach is developed toward both the restoration and the blind restoration problems. Such a (nontraditional) approach is based on the work of Sheppard et al [6], [7], Freeman and Pasztor [8], Baker and Kanade [9], and Panchapakesan et al [10]. The basic idea in such an approach is that the prior knowledge required for solving various (inverse) problems can be learned from training data, i.e., set of prototype images belonging to the same (statistical) class of images with the ones to be processed.…”

Section: Introductionmentioning

confidence: 99%

“…The basic idea in such an approach is that the prior knowledge required for solving various (inverse) problems can be learned from training data, i.e., set of prototype images belonging to the same (statistical) class of images with the ones to be processed. This idea was applied in [6] to the image restoration problem, in [7] to the super-resolution problem, by designing in both cases vector quantizers (VQ), in [8] to scene estimation (motion estimation and resolution enhancement) combined with Bayesian methods to propagate local measurement information, in [9] to the super-resolution problem, and in [10] to the blur identification problem.…”

Section: Introductionmentioning

confidence: 99%

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A VQ-based blind image restoration algorithm

Nakagaki

Katsaggelos

2003

IEEE Trans. on Image Process.

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In this paper, learning-based algorithms for image restoration and blind image restoration are proposed. Such algorithms deviate from the traditional approaches in this area, by utilizing priors that are learned from similar images. Original images and their degraded versions by the known degradation operator (restoration problem) are utilized for designing the VQ codebooks. The codevectors are designed using the blurred images. For each such vector, the high frequency information obtained from the original images is also available. During restoration, the high frequency information of a given degraded image is estimated from its low frequency information based on the codebooks. For the blind restoration problem, a number of codebooks are designed corresponding to various versions of the blurring function. Given a noisy and blurred image, one of the codebooks is chosen based on a similarity measure, therefore providing the identification of the blur. To make the restoration process computationally efficient, the principal component analysis (PCA) and VQ-nearest neighbor approaches are utilized. Simulation results are presented to demonstrate the effectiveness of the proposed algorithms.

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Joint compression and restoration of images using wavelets and non-linear interpolative vector quantization

Panchapakesan

Bilgin²,

Marcellin³

et al.

Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181

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In this paper, we present a wavelet based non-linear interpolative vector quantization scheme for joint compression and restoration of images; two tasks which are traditionally regarded as having conflicting goals. Vector quantizer codebook training is done using a training set consjsting of pairs of the original image and its difiaction-limited counterpart. The designed VQ is then used to compress and simultaneously restore diffraction-limited images. Results from simulations indicate that the image produced at the output of the decoder is quantitatively and visually superior to the diffraction-limited image at the input to the encoder. We also compare the performance of several wavelet filters in our algorithm.

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Lapped nonlinear interpolative vector quantization and image super-resolution

Cited by 3 publications

References 7 publications

Image interpolation using interpolative classified vector quantization

Image interpolation using interpolative classified vector quantization

A VQ-based blind image restoration algorithm

Joint compression and restoration of images using wavelets and non-linear interpolative vector quantization

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