Medical Image Contrast Enhancement using Range Limited Weighted Histogram Equalization

Adaptive Entropy Index Histogram Equalization for Poor Contrast Images

“…Dominant gray levels have lesser weight than the original histogram after the modification stage, thereby resulting in minimal improvements in the resultant image captured by WHE compared with those obtained by the three aforementioned CHE sub-classes. The contrast enhancement techniques developed under WHE include modified histogram equalization for contrast enhancement (MHE) [11], weighted threshold histogram equalization (WTHE) [30], recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement (RSWHE) [31], range limited weighted histogram equalization (RLWHE) [4], edge preservation local histogram equalization (EPLHE) [32], a logarithmic law-based histogram modification scheme for naturalness image contrast enhancement [33], a bi-histogram modification method for images with non-uniform illumination and low contrast [34], image enhancement via sub-image histogram equalization based on mean and variance (MVSIHE) [19] MHE is designed to prevent the formation of artifacts in RMSHE, DSIHE, and BBHE and to improve the appearance of the resultant image. This technique alters the histogram of the original image and then applies CHE to enhance contrast.…”

“…Contrast improvement is important in image quality assessment and has been widely applied in medical, satellite, and military images, among others [3]. The intensity of pixels in an image is modified by using contrast enhancement techniques, and the imparity between the foreground and background regions is increased in the process [4]. The techniques used for improving image contrast can be classified into (i) frequency and (ii) spatial domains [3].…”

Non-Contact Water Level Response Measurement of a Tubular Level Gauge Using Image Signals

“…In the histograms of dark images with low light intensity and of bright images with high light intensity, the distribution of pixels is inclined to one side or concentrated at a specific position [35]. For an image with a concentrated pixel distribution, it is difficult to distinguish the structure from the background because the pixels have similar light intensities [36,37]. Therefore, in this study, the structure was distinguished from the background by performing histogram transformation to measure the response of the tubular level gauge.…”

An Automatic Diagnosis Scheme for Gliomas Using Gray Level Co- occurrence Matrix, Histogram of Oriented Gradient,Discrete Wavelet Transformation, Intensity-based Features andHierarchical PSO-SVM

“…Histogram equalization (HE) is another kind of typical contrast enhancement technique, and the main idea of HE is to map the pixels in input images to the pixels in output images, of which the gray levels are in a wider range. 16 The output image after histogram equalization is shown in Fig. 3 (f), the image is further enhanced.…”