Image Compression Using Spline Based Wavelet Transforms

Averbuch, Amir; Zheludev, Valery A.

doi:10.1007/978-94-015-9715-9_12

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…The source images are not corrupted by noise or errors. The spline 5∕3 wavelet basis 23 was The first group of source images involves those with eccentric focus, the second contains images of visible contrasting and infrared light. Fig.…”

Section: Fusion Of Clean Imagesmentioning

confidence: 99%

Pixel-level multisensor image fusion based on matrix completion and robust principal component analysis

Wang

Deller

Fleet

2016

J. Electron. Imaging

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Abstract. Acquired digital images are often corrupted by a lack of camera focus, faulty illumination, or missing data. An algorithm is presented for fusion of multiple corrupted images of a scene using the lifting wavelet transform. The method employs adaptive fusion arithmetic based on matrix completion and self-adaptive regional variance estimation. Characteristics of the wavelet coefficients are used to adaptively select fusion rules. Robust principal component analysis is applied to low-frequency image components, and regional variance estimation is applied to high-frequency components. Experiments reveal that the method is effective for multifocus, visible-light, and infrared image fusion. Compared with traditional algorithms, the new algorithm not only increases the amount of preserved information and clarity but also improves robustness. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Fusion Of Clean Imagesmentioning

confidence: 99%