X-ray Image Contrast Enhancement Using the Second Generation Curvelet Transform

Li, Hao; Huo, Guanying

doi:10.1007/978-3-642-31588-6_46

Cited by 2 publications

(1 citation statement)

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“…In [1] H. Li, et, al: Proposes a novel X-ray image contrast enhancement method using the second generation curve let transform in order to better enhance contrast and edges while remove noise. Decompose images source in the curvelet transform domain.…”

Section: IImentioning

confidence: 99%

Enhancement of Medical Image by Fusion Method using Fast Discrete Curvelet Transform

Chinnadurai¹

2019

IJRASET

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Image fusion is the combination of two (or) more different image to form a new image with better information. Image resolution enhancement based on Fast Discrete Curvelet Transform. The info pictures are gotten from various envisioning methods such as Computer Tomography (CT), Positron Emission Tomography (PET) and Magnetic Resonance Imagining (MRI).Info picture is disintegrated into various subgroups. After decay, a non-straight capacity is connected to the produced curvelet co-proficient to improve the distinctive recurrence subgroups. The resultant images are fused by fusion techniques such as Principle Component analysis(PCA), Minimum selection method ,Maximum selection method and Simple average method. Fused image results was evaluated using metrics of Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR) and Entropy (H).The outcomes demonstrated that the resultant picture has preferable data over the current strategy which respect to visual quality and information substance of consolidated picture. The results procured can be helpful for remedial finding of patient for further treatment.

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Section: IImentioning

confidence: 99%