A New Texture Synthesis Algorithm Based on Wavelet Packet Tree

Hsin, Hsi-Chin; Sung, Tze Yun; Shieh, Yaw Shih; Cattani, Carlo

doi:10.1155/2012/305384

Cited by 5 publications

(9 citation statements)

References 30 publications

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“…In [19], we rearranged WP coefficients to form hierarchical trees called WP trees for forward texture synthesis. More specifically, pairs of WP coefficients d 2r ℓ+1 (n) and d 2r+1 ℓ+1 (n) obtained from a sequence of wavelet coefficients d r ℓ (n) can be rearranged and combined into a single sequencẽ Fig.…”

Section: Wp Treementioning

confidence: 99%

Inverse texture synthesis in wavelet packet trees

Hsin

Sung

2015

IET Computer Vision

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Inverse texture synthesis (ITS) plays an important role in many computer vision applications. It aims at generating small compaction images to capture significant features of textures, from which arbitrarily large images with similar textures can be re‐synthesised. This study presents a fast approach to ITS with two algorithms developed in hierarchical wavelet packet (WP) trees: the modified inverse texture synthesis (MITS) algorithm, which extends the ITS algorithm into the WP domain, and the wavelet‐packet‐tree‐based cropping (WPTC) algorithm for the initialisation of MITS. Experimental results show that the combination of WPTC and MITS termed the image compaction in wavelet packet trees algorithm outperforms the ITS algorithm in terms of the ‘peak signal‐to‐noise ratio’ and computation time.

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Section: Wp Treementioning

confidence: 99%

Inverse texture synthesis in wavelet packet trees

Hsin

Sung

2015

IET Computer Vision

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“…where ℎ( ) and ( ) are the low-pass and high-pass wavelet filters, respectively, and̃ℓ ,1 ( ) and̃ℓ ,2 ( ) are wavelet packet coefficients (WPC), which can be efficiently combined into a single sequence [35]:…”

Section: Hierarchical Wavelet Packet Treesmentioning

confidence: 99%

“…In [35] we proposed the TSIWPT algorithm to synthesize a large textured image in wavelet packet trees. To reduce the computation time, TSIWPT minimizes the difference between the output wavelet packet trees and the input wavelet packet trees based on their respective luminance components only.…”

Section: Accelerationmentioning

confidence: 99%

“…Wavelet packet transform extends wavelet transform by including more basis functions [27]. In [35], we adopted an efficient scheme to organize wavelet packet coefficients into hierarchical trees, based on which an efficient algorithm had been proposed for texture synthesis. Though wavelet packet transform is preferable to wavelet transform in terms of the representational diversity, the dominant components of an image may only be distributed in parts of the wavelet subbands.…”

Section: Introductionmentioning

confidence: 99%

“…Synthesis results of the input images (1st column) using Efros's algorithm[11] (2nd column), Cui's algorithm[33] (3rd column), and the TSIAWPT algorithm (4th column). Computation times of Efros's algorithm[11] (solid line), Cui's algorithm[33] (dashed line), TSIWPT[35] (dashdot line), and TSIAWPT (dotted line).…”

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confidence: 99%

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Fast Texture Synthesis in Adaptive Wavelet Packet Trees

Juang

Hsin

Sung

et al. 2013

Mathematical Problems in Engineering

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Wavelet packet transform known as a substantial extension of wavelet transform has drawn a lot of attention to visual applications. In this paper, we advocate using adaptive wavelet packet transform for texture synthesis. The adaptive wavelet packet coefficients of an image are organized into hierarchical trees called adaptive wavelet packet trees, based on which an efficient algorithm has been proposed to speed up the synthesis process, from the low-frequency tree nodes representing the global characteristics of textures to the high-frequency tree nodes representing the local details. Experimental results show that the texture synthesis in the adaptive wavelet packet trees (TSIAWPT) algorithm is suitable for a variety of textures and is preferable in terms of computation time.

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Spatial verification using wavelet transforms: a review

Weniger

Kapp

Friederichs

2016

Quart J Royal Meteoro Soc

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Due to the emergence of new high-resolution numerical weather prediction (NWP) models and the availability of new or more reliable remote sensing data, the importance of efficient spatial verification techniques is growing. Wavelet transforms offer an effective framework to decompose spatial data into separate (and possibly orthogonal) scales and directions. Most wavelet-based spatial verification techniques have been developed or refined in the last decade and concentrate on assessing forecast performance (i.e. forecast skill or forecast error) on distinct physical scales. Particularly during the last 5 years, a significant growth in meteorological applications could be observed. However, a comparison with other scientific fields, such as feature detection, image fusion, texture analysis or facial and biometric recognition, shows that there is still a considerable, currently unused potential to derive useful diagnostic information. In order to tap the full potential of wavelet analysis, we review the state-of-the-art in one-and two-dimensional wavelet analysis and its application, with emphasis on spatial verification. We further use a technique developed for texture analysis in the context of high-resolution quantitative precipitation forecasts, which is able to assess the structural characteristics of the precipitation fields and allows efficient clustering of ensemble data.

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A New Texture Synthesis Algorithm Based on Wavelet Packet Tree

Cited by 5 publications

References 30 publications

Inverse texture synthesis in wavelet packet trees

Inverse texture synthesis in wavelet packet trees

Fast Texture Synthesis in Adaptive Wavelet Packet Trees

Spatial verification using wavelet transforms: a review

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