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Comparative efficiency of color models for multi-focus color image fusion
(2010-12-01)
Rattanapitak, Wirat
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Udomhunsakul, Somkait
The comparative efficiency of color models for multi-focus color image fusion is presented in this paper. The objective of these experiments is to finding the proper color model for using in multi-focus color image fusion. In our research study, firstly we transform RGB color model of source images into four color models that are YIQ, YCbCr, HSV and HSI color models. Next, the intensity or luminance component is only used in fusion process using Spatial Frequency Measurement based fusion method compared with Stationary Wavelet Transform with Extended Spatial Frequency Measurement. Finally, the fused image results are transformed back to RGB model to get the final results. The experiments show that the YCbCr color model outperforms other color models in term of objective quality assessment.
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Comparative efficiency of Wavelet filters for multi-focus color image fusion
(2010-09-02)
Toontham, Jaruwan
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Rattanapitak, Wirat
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Udomhunsakul, Somkait
The comparative efficiency of Wavelet filters for multi-focus color image fusion is presented in this paper. Our experiment purposes are study and examine the suitable mother wavelets for using in multi-focus color image fusion. In fusion process, we use HSI color model and the fusing technique based on Stationary Wavelet Transform with Extended Spatial Frequency Measurement method. In our experiments, we investigate the effect of applying different types of wavelet filters belonging to orthogonal and biorthogonal wavelets with different orders. The wavelet filter used are Daubichies4, 8, 10, 12, 14, 16, 18 and Biorthogonal2.2, 2.4, 2.6, 2.8, 3.1, 3.3, 3.5, 3.7, 3.9, 4.4, 5.5, and 6.8. The fused image quality assessment is measured using Peak Signal to Noise Ratio. The result indicated that Biorthogonal 3.3 and 3.5 are proper choices for multi-focus color image fusion. © 2010 IEEE.
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Line detection fusion using spatial frequency measurement
(2010-05-28)
Toontham, Kitti
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Emapana, Bhurit
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Udomhunsakul, Somkait
In this paper, we present a line detection fusion method using Spatial Frequency Measurement (SFM). Our proposed method, Á trous algorithm is firstly applied with the original image to get the line feature detail information in two difference resolutions from a coarse one with 5x5 mask and a fine one with 3x3 mask. Next, both line feature detail information are fused to get a complete line feature using Spatial Frequency Measurement, which is compared with Laplacian Operator. From the experiments, we found that our proposed method provides a complete line feature evaluated by the correlation value. Also, the method is general and can be applied to other features in imagery. ©2010 IEEE.
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Selection of wavelet filters for panoramic dental x-ray image compression
(2009-04-20)
Borwonwatanadelok, Pusit
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Purahong, Boonchana
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Udomhunsakul, Somkait
Recently, wavelet transform has proven to be very effective in medical image compression. However, the choice of wavelet filters is a vital factor that can be determined the compression performance. The aim of this paper is to investigate the effect of applying different types of wavelet filters belonging to orthogonal and biorthogonal wavelets with different orders on the panoramic dental x-ray images. The wavelet filters used are Haar, Daubichies8, 6/10, 9/7 and 5/3 filters. The performance evaluation of the image quality is measured objectively using peak signal to noise ratio and blocking artifact measurement. The almost simulated results reveal that 9/7 irreversible wavelet transform is the best choice for lossy panoramic dental x-ray image compression. © 2009 IEEE.
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Multi-focus image fusion based on Stationary Wavelet Transform and extended Spatial Frequency Measurement
(2009-04-20)
Borwonwatanadelok, Pusit
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Rattanapitak, Wirat
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Udomhunsakul, Somkait
In this paper, we propose a multi-focus image fusion approach based on Stationary Wavelet Transform (SWT) and extended the Spatial Frequency Measurements (SFM). Our proposed approach, two fused images are firstly decomposed into four subbands, which are one approximation subband (LL) and three details subbands (HL, LH and HH). Next, each subband is partitioned into blocks and each block is identified the clearer regions by computing the focus measure using the extended Spatial Frequency Measurement (SFM). Finally, the recovered fused image is reconstructed by performing the Inverse Stationary Wavelet Transform. From the experimental results, we found that the proposed method outperforms the traditional Wavelet Transform and SFM based methods in terms of objective and subjective assessments. © 2009 IEEE.
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Image quality assessment for JPEG and JPEG2000
(2008-12-29)
Sakuldee, Ratchakit
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Yamsang, Nuntapong
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Udomhunsakul, Somkait
Assessment of the compressed image quality is an important issue for image processing system. In this paper, we propose a novel objective assessment to measure the quality of gray scale compressed image, which is developed from the fundamental objective measurement. It is also correlated well with human response and least time taken comparable to some existing measurements. The proposed measurement, Spatial Frequency Measurement (SFM) is adopted to enhance the image quality evaluation performance. From the experimental results, we found that Mean Average Error (MAE) with SFM (MAESFM) is the suitable measurements that can be used to measure the quality of JPEG and JPEG2000 compressed images. In addition, MAESFM measurement is scaled to make them equivalent to MOS, given the rate of compressed image quality from 1 to 5 (unacceptable to excellent quality). © 2008 IEEE.
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Objective measurements of distorted image quality evaluation
(2008-09-22)
Sakuldee, Ratchakit
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Udomhunsakul, Somkait
Measurement of the quality of distorted image still remains an important issue. In this paper, we propose a new objective measurement, denoted as Image Quality Score (IQS) that better matches the Visual Information Fidelity (VIF) than using fundamental image quality measurements. IQS can be used to measure the quality of gray scale image in different distortion types such as blurring, additive Gaussian noise, impulsive salt & pepper noise and JPEG2000 images. From the experiments, we found that IQS correlates well with the judgment of human observers. Moreover, it gives the result of distorted image quality from 1 to 5 (unacceptable to excellent quality). ©2008 IEEE.
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Rock images classification using Principle Component Analysis and Spatial Frequency Measurement
(2008-03-31)
Kachanubal, Tossaporn
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Udomhunsakul, Somkait
Since the natural rocks have quite different textures even they are in the same class, it is very difficult and challenging task to classify each type of natural rocks. In this paper, we present a method to classify each type of rocks using the modified version of Spatial Frequency Measurement (SFM). In our approach, each type of color rock images are firstly transformed into two dimensional intensity features, obtained from the highest and lowest eigenvalues of the Principle Component Analysis (PCA). The highest and lowest eigenvalues are corresponded to the most and least significant feature components. Next, the textural contents of each component are measured using the modified version of SFM, which measures all overall activity level of each component in two directions including vertical, horizontal directions by shifting one by one pixel for two-neighborhood pixels in both direction. Before applying modified version of SFM, the edge detection operator, Sobel operator, is applied to the most significant component only. After applying the modified version of SFM to both components, two textural features are used to define each type of rock. In our experiments, we test our approach to classify on 14 different classes of rock textures, each class has 30 samples. From the results, we found that the scatter plots of each type of rock features are obviously grouped and stuck together in the same class while the different classes are clearly separated.
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Despeckling algorithm on ultrasonic image using adaptive block-based singular value decomposition
(2008-03-31)
Sae-Bae, Napa
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Udomhunsakul, Somkait
Speckle noise reduction is an important technique to enhance the quality of ultrasonic image. In this paper, a despeckling algorithm based on an adaptive block-based singular value decomposition filtering (BSVD) applied on ultrasonic images is presented. Instead of applying BSVD directly to ultrasonic image, we propose to apply BSVD on the noisy edge image version obtained from the difference between the logarithmic transformations of the original image and blur image version of its. The recovered image is performed by combining the speckle noise-free edge image with blur image version of its. Finally, exponential transformation is applied in order to get the reconstructed image. To evaluate our algorithm compared with well-know algorithms such as Lee filter, Kuan filter, Homomorphic Wiener filter, median filter and wavelet son thresholding, four image quality measurements, which are Mean Square Error (MSE), Signal to MSE (S/MSE), Edge preservation (β), and Correlation measurement (ρ), are used. From the results, it clearly shows that the proposed algorithm outperforms other methods in terms of quantitative and subjective assessments.
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Influence of SFM to objective assessment of compressed image
(2007-12-31)
Yamsang, Nuntapong
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Udomhunsakul, Somkait
Evaluation of the quality of image compression still remains an important issue. In this paper, we propose a novel objective assessment to evaluate the quality of gray scale compressed images. An image characteristic measurement, Spatial Frequency Measurement (SFM), is employed to get the new objective measurements, which are Mean Square Error with SFM (MSESFM), Edge Measurement with SFM (ESFM) and Correlation Measurement with SFM (CSFM). From the experiments, we found that SFM influences to the objective assessment. In addition, the reliabilities of the new measures have been improved and better match the subjective assessment than using the traditionally simple objective assessments. © 2007 IEEE.

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