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    Comparison of image enhancement techniques and CNN models for COVID-19 classification using chest x-rays images
    (2022-01-01)
    Kanjanasurat, Isoon
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    Domepananakorn, Nontacha
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    Archevapanich, Tuanjai
    ;
    Purahong, Boonchana
    This paper compares two image enhancement techniques with five convolutional neural network (CNN) models to classify Covid-19 chest x-ray images. a contrast limited adaptive histogram (CLAHE) and gamma correction which is method to improve image histogram are compared with the original chest x-ray image. We use five publicly available pre-trained CNN models to detect COVID-19: MobileNet, MobileNetV2, DenseNet169, DenseNet201, and ResNet50V2. Our procedure was validated using the COVID-19 radiography database, which is a freely accessible resource. MoblileNet with gamma correction is well-suited for COVIC-19 classification, achieving an accuracy score of 87.53 percent on the first epoch and 95.46 percent after training 100 epochs with the shortest computation time.
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    Variance training data in image enhancement
    (2019-07-01)
    Ngernplubpla, Jaturon
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    Chitsobhuk, Orachat
    This paper presents a study of neuro-fuzzy behavior in clustering gradient profile spectral characteristics. Various types of image scene are chosen to evaluate neuro-fuzzy performance. The combinations of training data subsets are learned by ANFIS model to generate gradient profile priors, which are used as optimum weight selection criteria for image enhancement. The experimental results illustrate quantitative performance improvement and perceptual improvement in recovery of the high-resolution details in various images.
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    Image enhancement based on edge boosting algorithm
    (2015-01-01)
    Ngernplubpla, Jaturon
    ;
    Chitsobhuk, Orachat
    In this paper, a technique for image enhancement based on proposed edge boosting algorithm to reconstruct high quality image from a single low resolution image is described. The difficulty in single-image super-resolution is that the generic image priors resided in the low resolution input image may not be sufficient to generate the effective solutions. In order to achieve a success in super-resolution reconstruction, efficient prior knowledge should be estimated. The statistics of gradient priors in terms of priority map based on separable gradient estimation, maximum likelihood edge estimation, and local variance are introduced. The proposed edge boosting algorithm takes advantages of these gradient statistics to select the appropriate enhancement weights. The larger weights are applied to the higher frequency details while the low frequency details are smoothed. From the experimental results, the significant performance improvement quantitatively and perceptually is illustrated. It can be seen that the proposed edge boosting algorithm demonstrates high quality results with fewer artifacts, sharper edges, superior texture areas, and finer detail with low noise.
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    Linear filtering for optimized approach in satellite image enhancement
    (2010-11-11)
    Pattanasethanon, Petcharat
    ;
    Attachoo, Boonwat
    Problem statement: For decades, several image enhancement techniques have been proposed. Although most techniques require profuse amount of advance and critical steps, the result for the perceive image are not as satisfied. Approach: In this study, we proposed a new method to enhance the satellite image which compares two procedures using two different kinds of filtering technique with an additional step in order to obtain the perceived image. In this new algorithm we first transform the color image into grayscale. The image is then preceded to the edge detection and brightness enhancement step using Laplacian and Sobel technique individually. Results: From the results, the Tenengrad averred that the enhancement result of the dimension and depth in the image were successfully classified. We also evaluate the image quality, adjusting by the PSNR and Tenengrad criterion which indicates that the proposed method shows dramatically increase in pixel distribution throughout the range of RGB. Conclusion: The result of this research is also beneficial in terms of geographical views due to the process which determined the difference appeared on each area. Eventually, this research also performed a comparison for the enhancement step mentioned in this study. © 2010 Science Publications.
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    A new approach for colored satellite image enhancement
    (2008-01-01)
    Attachoo, Boonwat
    ;
    Pattanasethanon, Petcharat
    A unique method of image filtering has been developed that enhances the detail and sharpens the edges of colored satellite images. Histogram equalization coupled with a two stage data filtering process that applies convolution with laplacian and sharpening with laplacaian through the 3 color bands that produce the colored satellite images has yielded sharper clearer images. The initial enhancement using histogram equalization was followed by the first stage of a filtering process convolution with laplacian which highlighted the edges of the image. The application of the second stage filtering sharpening with laplacian yielded enhanced color reproduction and a more accurate depiction of information at sea and land levels than was available in the original image. An analysis of the statistical index and signal to noise ratio of the true color and false color of histogram equalization, convolution with laplacian and sharpening images showed the image. An analysis of the false color of histogram equalization, convolution with laplacian and sharpening images showed the image to be superior, in this study the multi spectral content in the satellite image was transformed into a composite colour image, and then convoluted using the laplacian technique. This study placed an emphasis on improving the detail and edge clarity of satellite images. © 2008 IEEE.
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    Local area histogram equalization based multispectral image enhancement from clustering using competitive hopfield neural network
    (2003-10-01)
    Chitwong, S.
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    Boonmee, T.
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    Cheevasuvit, F.
    One of important issues for enhancing image based on local area histogram equalization (LHE) is a clustering or segmenting technique. That is, the more the accuracy of separating image into specified classes is needed, the better the performance of enhancement is. As mentioned objective, in this paper, the competitive Hopfield neural network (CHNN) is then proposed for clustering to the LHE based image enhancement. By using simulated image, standard image and mutispectral image from Landsat 7 satellite, experimental results are shown in both accuracy of clustering and variance of the enhanced image. The criteria for a good enhancement algorithm is that it can give high variance in detail area, low variance in smooth and edge areas. Also comparing the variance of the enhanced image by both LHE and global area histogram equalization (GHE) methods shows that one from LHE outperforms. In addition, the enlarged image from small area is shown clearly by visualization. All results compare with the conventional methods such as fuzzy c-means (FCM).
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    Enhancement of color image obtained from PCA-FCM technique using local area histogram equalization
    (2002-12-01)
    Chitwong, S.
    ;
    Boonmee, T.
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    Cheevasuvit, F.
    This paper presents local area enhancement of the segmented color image obtained from the multi-spectral image clustering by using FCM (fuzzy c-means). In case, the multi-spectral images, which have the number of bands more than that of 3, must decrease the data volume to remain the number of bands of 3 in order to correspond with the meaning of red, green, and blue images. PCA (Principal Components Analysis) is then used to transform original multi-spectral images into PCA images. The first three components having information more than that of original images of 95% is assigned as red, green, and blue images, namely RGB color image. FCM clustering apply to RGB color image, separately. This method is called the PCA-FCM technique being the multi-spectral image clustering. By applying such technique, the result images consisted of red, green, and blue images separately are the segmented images. By histogram equalization algorithm, the result of local area enhancement based on a number of clusters as the segmented image can solve effect of intensity saturation from global area enhancement and the perceptibility of color image is clearly improved.