earticle

영어

By using an adaptive threshold and weight based on the wavelet transform and Haar transform, a novel image enhancement algorithm is proposed. First, a medical image was decomposed with wavelet transform and all high-frequency sub-images were decomposed with Haar transform. Secondly, noise in the frequency domain was reduced by the proposed soft-threshold method. Thirdly, high-frequency coefficients were enhanced by the proposed weight values in different sub-images. Then, the enhanced image was obtained through the inverse Haar transform and wavelet transform. But the pixel range of the enhanced image is narrower than a normal image. Lastly, the image’s histogram was stretched by nonlinear histogram equalization. Experiments showed that the proposed method can be not only enhance an image’s details but can also preserve its edge features effectively.

한국어

본 논문에서는 웨이블릿 변환과 Haar 변환을 기반으로 적응적 문턱치와 가중치를 이용하여 의료영상의 화질 을 개선하는 알고리즘을 제안한다. 첫째, 화질이 저하된 의료영상에 대해 웨이블릿 변환을 수행하고 분해된 고주파 밴드에 대해 Haar 변환을 수행한다. 둘째, 고주파 각 밴드에 대해 적응적 문턱치를 이용하여 잡음을 제거한다. 셋째, 잡음이 제거된 고주파 밴드에 대해 적응적인 가중치를 이용하여 계수를 향상한 후, Haar 역변환 및 웨이블릿 역변환 을 수행하여 복원영상을 얻는다. 마지막 단계에서는 복원된 영상의 화소 값의 범위가 좁아졌으므로 비선형 히스토그 램 평활을 이용하여 화소 값의 범위를 조절하고 명암 대비가 좋은 향상된 영상을 얻는다.