Post-sampling aliasing control for natural images

Florêncio, Dinei; Schafer, Ronald W.

doi:10.1109/icassp.1995.480318

Cited by 11 publications

(3 citation statements)

References 9 publications

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“…An arbitrary set of sequences is not necessarily the wavelet transform of some function in . It is the wavelet transform of some function if and only if (6) If the set of sequences satisfies (6), then we say that it belongs to the range of the wavelet transform operator . The operator is thus the projection operator onto the range of the wavelet transform.…”

Section: A Discretization Issuesmentioning

confidence: 99%

Locally adaptive wavelet-based image interpolation

Chang¹,

Cvetković

Vetterli

2006

IEEE Trans. on Image Process.

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Abstract-We describe a spatially adaptive algorithm for image interpolation. The algorithm uses a wavelet transform to extract information about sharp variations in the low-resolution image and then implicitly applies interpolation which adapts to the image local smoothness/singularity characteristics. The proposed algorithm yields images that are sharper compared to several other methods that we have considered in this paper. Better performance comes at the expense of higher complexity.

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Section: A Discretization Issuesmentioning

confidence: 99%

Locally adaptive wavelet-based image interpolation

Chang¹,

Cvetković

Vetterli

2006

IEEE Trans. on Image Process.

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“…Edge-directed interpolation techniques [9], [10] employ a source model that emphasizes the visual integrity of the detected edges and modify the interpolation to fit the source model. Other approaches [11], [12] borrow the techniques from vector quantization (VQ) and morphological filtering to facilitate the induction of high-resolution images.…”

Section: Introductionmentioning

confidence: 99%

New edge-directed interpolation

Li¹,

Orchard

2001

IEEE Trans. on Image Process.

1,719

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This paper proposes an edge-directed interpolation algorithm for natural images. The basic idea is to first estimate local covariance coefficients from a low-resolution image and then use these covariance estimates to adapt the interpolation at a higher resolution based on the geometric duality between the low-resolution covariance and the high-resolution covariance. The edge-directed property of covariance-based adaptation attributes to its capability of tuning the interpolation coefficients to match an arbitrarily oriented step edge. A hybrid approach of switching between bilinear interpolation and covariance-based adaptive interpolation is proposed to reduce the overall computational complexity. Two important applications of the new interpolation algorithm are studied: resolution enhancement of grayscale images and reconstruction of color images from CCD samples. Simulation results demonstrate that our new interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional linear interpolation.

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“…As known, there are several issues to take into account about zooming: unavoidable smoothing effects, reconstruction of high frequency details without the introduction of artifacts and computational efficiency. Several good zooming techniques are nowadays well known [9], [10], [13], [16], [18], [22].…”

Section: Introductionmentioning

confidence: 99%