Browsing by Author "Shyam, L."

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    Automatic method for contrast enhancement of natural color images
    (2015) Shyam, L.; Narasimhadhan, A.V.
    The contrast enhancement is great challenge in the image processing when images are suffering from poor contrast problem. Therefore, in order to overcome this problem an automatic method is proposed for contrast enhancement of natural color images. The proposed method consist of two stages: in first stage lightness component in YIQ color space is normalized by sigmoid function after the adaptive histogram equalization is applied on Y component and in second stage automatic color contrast enhancement algorithm is applied on output of the first stage. The proposed algorithm is tested on different NASA color images, hyperspectral color images and other types of natural color images. The performance of proposed algorithm is evaluated and compared with the other existing contrast enhancement algorithms in terms of colorfulness metric and color enhancement factor. The higher values of colorfulness metric and color enhancement factor imply that the visual quality of the enhanced image is good. Simulation results demonstrate that proposed algorithm provides higher values of colorfulness metric and color enhancement factor as compared to other existing contrast enhancement algorithms. The proposed algorithm also provides better visual enhancement results as compared with the other existing contrast enhancement algorithms. The Korean Institute of Electrical Engineers.
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    Automatic method for contrast enhancement of natural color images
    (Korean Institute of Electrical Engineers, 2015) Shyam, L.; Narasimhadhan, A.V.
    The contrast enhancement is great challenge in the image processing when images are suffering from poor contrast problem. Therefore, in order to overcome this problem an automatic method is proposed for contrast enhancement of natural color images. The proposed method consist of two stages: in first stage lightness component in YIQ color space is normalized by sigmoid function after the adaptive histogram equalization is applied on Y component and in second stage automatic color contrast enhancement algorithm is applied on output of the first stage. The proposed algorithm is tested on different NASA color images, hyperspectral color images and other types of natural color images. The performance of proposed algorithm is evaluated and compared with the other existing contrast enhancement algorithms in terms of colorfulness metric and color enhancement factor. The higher values of colorfulness metric and color enhancement factor imply that the visual quality of the enhanced image is good. Simulation results demonstrate that proposed algorithm provides higher values of colorfulness metric and color enhancement factor as compared to other existing contrast enhancement algorithms. The proposed algorithm also provides better visual enhancement results as compared with the other existing contrast enhancement algorithms. © The Korean Institute of Electrical Engineers.
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    Enhanced JAYA optimization based medical image fusion in adaptive non subsampled shearlet transform domain
    (Elsevier B.V., 2022) Suresh, S.; Rajan, M.; Asha, C.S.; Shyam, L.
    Multi-modal image fusion has gained popularity in the medical field as it assists doctors to view the diverse medical image modalities in a single image. The treatment is effectively planned by looking into the fused image that helps doctors diagnose diseases. The medical image fusion aims to merge the texture features from multiple images in a single image. The proposed method includes the application of Adaptive window-based Non-Subsampled Shearlet Transform (ANSST) on source images to separate the low and high-frequency directional sub-bands. Further, an enhanced JAYA (EJAYA) optimization framework is utilized to obtain the adaptive weights for combining high-frequency sub-bands for a multi-modal medical image fusion. The low-frequency bands are fused using the max rule based on the average energy of low-frequency sub-bands. The entire process focuses on preserving the low-frequency band's energy while improving the texture details in the combined image. In the end, inverse ANSST is applied on merged low-frequency and high-frequency components to get the fused image. Extensive experiments are conducted on data sets obtained from the Brain Atlas website comprising more than 100 images. The significance of the current approach is validated by qualitative and quantitative assessments. The proposed method exhibits good performance in terms of subjective analysis compared to the recent well-known image fusion techniques. © 2022 Karabuk University