Efficient image resizing using finite differences

Munoz, A.; Blu, Thierry; Unser, Michaël

doi:10.1109/icip.1999.817198

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…According the Shannon sampling theorem, once image contains frequencies above the Nyquist limit the information lost will be inevitable. As such, global based image reduction method has been proposed by scholars, such as discrete fuzzy transform base image reduction method [2], least squared trimming based image reduction method [3], minimum cosine variation correction based image reduction method [4] and so on. Compare with the traditional image reduction technique those global based image reduction methods has significantly improves the quality of the reduced image, however, those global based image reduction methods takes the full image as the input resort to the computational cost and hardware requirements relatively high, do not suitable for large-scale image reduction tasks.…”

Section: Introductionmentioning

confidence: 99%

Background Self-adaptive Weakly-monotonic Averaging Image Reduction Operator

2018

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Image reduction can simplify the raw image while preserve the structure feature and details information of pictures. Traditional monotonic averaging-based image reduction operator usually lose the detail features of the raw image after reduction. Recent proposed weakly-monotone image reduction algorithm need to specify the background colour manually in the image reduction process, if the background colour of the image to be reduced is not consistent with the former specified colour, this method does not work as expect. For filling this research gap, a new background adaptive weakly-monotonic averaging image reduction operator which can identify the background and adjust the weight according to the pixel distribution of the image was proposed in this paper. The experiment shows that compared with the previous image reduction operator, it has a better applicability and robustness.

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Section: Introductionmentioning

confidence: 99%

Background Self-adaptive Weakly-monotonic Averaging Image Reduction Operator

2018

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“…Due to these properties, several approaches have been introduced for effective image interpolation and reduction [3], [4], . [5]. Furthermore, several attempts have been proposed for improving the B-splines approximation capabilities.…”

Section: Introductionmentioning

confidence: 99%

Edge-preserving image resizing using modified B-splines

Gotchev

Егиазарян

Vesma

et al.

2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221)

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An edge-preserving method for image resizing (decimation and interpolation) is proposed. The decimation is considered as an orthogonal projection with respect to the chosen interpolation basis. The latter one is formed in a spline-like manner as a linear combination of B-splines of different degrees. This combination is optimized in such a way that the small image details are preserved. Considering the strongest edges as step edges, a segmentation procedure preceding the decimation is proposed. It leads to resized images with clearly outlined borders.

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Spline-based resampling of noisy images

Gotchev

Егиазарян²

ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In Conjunction With 23rd

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Efficient image resizing using finite differences

Cited by 3 publications

References 9 publications

Background Self-adaptive Weakly-monotonic Averaging Image Reduction Operator

Background Self-adaptive Weakly-monotonic Averaging Image Reduction Operator

Edge-preserving image resizing using modified B-splines

Spline-based resampling of noisy images

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