In this paper, a novel triple clipped histogram model-based fusion approach has been proposed to improve the basics features, brightness preservation and contrast of the medical images. This incorporates the features of the equalized image and input image together. In the initial step, the low-contrast medical image is equalized using the triple clipped dynamic histogram equalization technique for which the histogram of the input medical image is split into three sections on the basis of standard deviation with almost equal number of pixels. The clipping process of the histogram is performed on every histogram section and mapped to a new dynamic range using simple calculations. In the second step, the sub-histogram equalization process is performed separately. Approximation and detail coefficients of equalized and input images are separated using discrete wavelet transform (DWT). Thereafter, the approximation coefficients are modified using some basic calculation-based fusion which involves singular value decomposition (SVD) and its inverse. Detail coefficients are fused using spatial frequency features. This yields modified approximation and detail coefficients for an enhanced image. Finally, inverse discrete wavelet transform (IDWT) has been applied to the modified coefficients which result in an enhanced image with improved visual quality. These improvements are analyzed qualitatively and quantitatively.

中文翻译：

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使用空间频率增强基于三重直方图的医学图像。

本文提出了一种新颖的基于三重裁剪直方图模型的融合方法，以改善医学图像的基本特征，亮度保持和对比度。这将均衡图像和输入图像的特征结合在一起。在初始步骤中，使用三重裁剪动态直方图均衡技术均衡低对比度的医学图像，对于该技术，输入的医学图像的直方图将基于标准差在像素数几乎相等的情况下分为三部分。在每个直方图部分执行直方图的裁剪过程，并使用简单的计算将其映射到新的动态范围。在第二步骤中，分别进行子直方图均衡处理。使用离散小波变换（DWT）分离均衡图像和输入图像的近似系数和细节系数。此后，使用一些基于基本计算的融合来修改近似系数，该融合涉及奇异值分解（SVD）及其逆运算。使用空间频率特征融合细节系数。这产生了用于增强图像的修改后的近似系数和细节系数。最后，逆离散小波变换（IDWT）已应用于修改后的系数，从而得到具有改进视觉质量的增强图像。对这些改进进行了定性和定量分析。使用空间频率特征融合细节系数。这产生了用于增强图像的修改后的近似系数和细节系数。最后，逆离散小波变换（IDWT）已应用于修改后的系数，从而得到具有改进视觉质量的增强图像。对这些改进进行了定性和定量分析。使用空间频率特征融合细节系数。这产生了用于增强图像的修改后的近似系数和细节系数。最后，逆离散小波变换（IDWT）已应用于修改后的系数，从而得到具有改进视觉质量的增强图像。对这些改进进行了定性和定量分析。