Image Enhancement using Wavelet Fusion for Medical Image Processing
M. Mahesh1, TR Revanth Kumar2, B.Shoban Babu3, J. Saikrishna4

1M. Mahesh*, ECE, Sri Venkateswara Engineering College, Tirupati, India.
2T R Revanth Kumar ECE, Sri Venkateswara Engineering College, Tirupati, India.
3Dr. B. Shoban Babu, ECE, Sri Venkateswara Engineering College, Tirupati, India.
4J. Saikrishna, ECE, Sri Venkateswara Engineering College, Tirupati, India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 2663-2667 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9885109119/2019©BEIESP | DOI: 10.35940/ijeat.A9885.109119
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Abstract: Medical Image Enhancement Low contrast is the active study area that the obtained pictures suffer from noise and low contrast. Age of capturing equipment, bad illumination circumstances are the low contrast medical images. Thus, techniques of contrast improved performance are used before being used to enhance the contrast of medical images. Within a tiny range of pixel concentrations, contrast improvement algorithms enhance low contrast image. Low contrast image enhancement is accomplished using Equalization of Contrast Limited Adaptive Histogram. CLAHE image enhancement is used to enhance the quality of medical images with low contrast. DWT image, sub-bands such as LL, LH, HL, HH are decomposed. 2D Adaptive fusion image on discrete wavelet transformation is used to fuse the main and CLAHE output images. The efficiency of the output is calculated using merged image entropy and PSNR. It is discovered that the visual content of low contrast medical pictures is enhanced effectively on the basis of 2D DWT and adaptive Fusion.
Keywords: AHE, CLAHE, DWT, PSNR, Adaptive Fusion, Entropy.