Mong-Shu Lee

Image similarity comparison using dual-tree wavelet transform

An image similarity comparison method for images with minor distortions is introduced in this paper. The proposed image similarity metrics is based on a new method to measure structure similarity for image quality comparisons. We make use of the fact that Dual-Tree wavelet Transform (DTWT) can provide direction selectivity and keep the structure features between the original and images with minor distortions. Despite the simplicity of our method, our experimental results demonstrate the effectiveness of the proposed method.

Image Compression And Watermarking By Wavelet Localization

This paper has presented two image processing applications, compression and watermarking, by exploiting the localization property of wavelet transform. The first application proposes a simple region-based and scalable image compression algorithm. We locate the wavelet coefficients in the region of interest in each subband, and these groups of wavelet coefficients are used to adjust the resolution of the interested region. A watermarking method is described in the second application of this paper. The scheme examines the variations of the local Hölder regularity of the image and calculates the similarity of the correct watermark before and after modifications. Experimental results show that the proposed approach is quite effective in authenticating the origin of an image.

Similarity Analysis of Time Series Gene Expression using Dual-Tree Wavelet Transform

This study presents a similarity-determining method for measuring regulatory relationships between pairs of genes from microarray time series data. The proposed similarity metrics are based on a new method to measure structural similarity to compare the quality of images. We make use of the fact that dual-tree wavelet transform (DTWT) can provide approximate shift invariance and maintain the structures between pairs of regulation-related time series expression data. Despite the simplicity of the presented method, experimental results demonstrate that it enhances the similarity index when tested on known transcriptional regulatory genes.

Signal smoothness estimation in hölder spaces

Previous research has shown that wavelet method can be used to estimate the Besov smoothness of a function (signal). This paper describes an algorithm that is based on the magnitudes of the wavelet coefficients and linear regression model to estimate the smoothness of different signals of one and two-dimensional in the Hölder spaces. Computational results show that the Holder smoothness of the general two-dimensional image is between 0.2 and 0.7. We compare our results with those in Besov smoothness spaces and discuss the smoothness relations between these two function spaces.

Region-based and scalable image compression by wavelet localization

In this paper a wavelet and region-based image compression scheme was proposed. The wavelet coefficients at each level related to a square or circular interested regions were accurately located. Methods of combining localization of wavelets with weighting factors are presented. This approach overcomes the difficulty arising from JPEG and exhibits the ability to allow users to construct the region of interest with PSNR scalability in a compressed image.

Wavelets-based smoothness comparisons for volume data

In this study, the authors describe an objective smoothness assessment method for volume data. The metric can predict the extent of the difference in smoothness between a reference model, which may not be of perfect quality, and a distorted version. The proposed metric is based on the wavelet characterisation of Besov function spaces. The comparison of Besov norms between two models can resolve the global and local differences in smoothness between them. Experimental results from volume datasets with smoothing and sharpening operations demonstrate its effectiveness. By comparing direct volume rendered images, the experimental results show that the proposed smoothness index correlates well with human perceived vision. Finally, the metric can help the analyse compression distortions when they compare volume data with different smoothness.

Wavelets-Based Smoothness Metric for Volume Data

In this paper we describe an objective smoothness assessment method for volume data. The metric can predict the extent of the difference in smoothness between a reference model, which may not be of perfect quality, and a distorted version. The proposed metric is based on the wavelet characterization of Besov function spaces. The comparison of Besov norm between two models can resolve the global and local differences in smoothness between them. Experimental results from volume datasets with smoothing and sharpening operations demonstrate its effectiveness. Also, the proposed smoothness index correlates well with human perceived vision when compared with direct volume rendered images.

Images matching based on edge maps and wavelet transform

A structure-based image similarity measurement called DTWT-SSIM is presented. The main idea behind DTWT-SSIM is to combine the shift-invariance advantage of dual-tree wavelet transform (DTWT) with the structure-preserving property of the structural similarity metrics (SSIM). A series of experimental results show the improved measurement to be an effective and stable metric in the comparison of edge maps when small noise and distortion appear in the images.

Similarity Matches of Gene Expression Data Based on Wavelet Transform

This study presents a similarity-determining method for measuring regulatory relationships between pairs of genes from microarray time series data. The proposed similarity metrics are based on a new method to measure structural similarity to compare the quality of images. We make use of the Dual-Tree Wavelet Transform (DTWT) since it provides approximate shift invariance and maintain the structures between pairs of regulation related time series expression data. Despite the simplicity of the presented method, experimental results demonstrate that it enhances the similarity index when tested on known transcriptional regulatory genes.

Face Recognition under Variant Illumination Using PCA and Wavelets

In this paper, an efficient wavelet subband representation method is proposed for face identification under varying illumination. In our presented method, prior to the traditional principal component analysis (PCA), we use wavelet transform to decompose the image into different frequency subbands, and a low-frequency subband with three secondary high-frequency subbands are used for PCA representations. Our aim is to compensate for the traditional wavelet-based methods by only selecting the most discriminating subband and neglecting the scattered characteristic of discriminating features. The proposed algorithm has been evaluated on the Yale Face Database B. Significant performance gains are attained.