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Image contrast enhancement using DWT-SVD based masking technique

机译:使用基于DWT-SVD的蒙版技术增强图像对比度

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A new approach based on masking technique for contrast enhancement of medical image is presented due to the low contrast characteristics. In medical image, preserving mean brightness, average information and noise factor reduction are essential to make input image more appealing visually. The proposed method incorporates spatial and frequency domain techniques to enhance the contrast of the medical image. The mask is formulated effectively between reconstructed approximation coefficients and inverse singular value decomposition (ISVD) to obtain contrast residual. Discrete wavelet transformation (DWT) and singular value decomposition (SVD) have used to decompose the input image. At last maximum contrast enhancement achieved by adding mask with the image obtained through intensity exposure histogram equalization (IEHE). The proposed approach is tested for medical images by comparing its peak signal to noise Ratio (PSNR), absolute mean brightness error (AMBE) and entropy with some existing methods and also measured in terms of visual quality.
机译:由于低对比度特性,提出了一种基于掩模技术的医学图像对比度增强新方法。在医学图像中,保持平均亮度,平均信息和降低噪声因子对于使输入图像在视觉上更具吸引力至关重要。所提出的方法结合了空间和频域技术以增强医学图像的对比度。有效地在重新构造的近似系数与反奇异值分解(ISVD)之间制定掩模,以获得对比度残差。离散小波变换(DWT)和奇异值分解(SVD)已用于分解输入图像。最后,通过将掩模与通过强度曝光直方图均衡化(IEHE)获得的图像相加,可以实现最大的对比度增强。通过将其峰值信噪比(PSNR),绝对平均亮度误差(AMBE)和熵与某些现有方法进行比较,并针对视觉质量进行了测量,对所提出的方法进行了医学图像测试。

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