Adaptive contrast enhancement and de-enhancement
References (21)
- et al.
Digital image enhancement: a survey
Comput. Graphics Image Process.
(1983) Image enhancement by histogram transformation
Comput. Graphics Image Process.
(1977)Image enhancement by histogram hyperbolization
Comput. Graphics Image Process.
(1977)- et al.
Transform amplitude sharpening: a new method of image enhancement
Comput. Vision Graphics Image Process.
(1987) - et al.
Rank algorithms for picture processing
Comput. Vision Graphics Image Process.
(1986) - et al.
Contrast enhancement technique based on local detection of edges
Comput. Vision Graphics Image Process.
(1989) - et al.
Adaptive histogram equalization and its variations
Comput. Vision Graphics Image Process.
(1987) - et al.
The concept of de-enhancement in digital image processing
Pattern Recognition Lett.
(1984) Digital Image Processing
(1979)
Cited by (55)
COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images
2022, Biocybernetics and Biomedical EngineeringCitation Excerpt :Besides, there is no clear boundary between the lesion region and normal lung tissue, where it is in a gradual transition state. To improve the negative and positive COVID-19 classification accuracy, the proposed algorithm enhances the contrast between the COVID-19 region and the normal region by an adaptive enhancement algorithm [46–49]. To overcome the problems in traditional deep learning, the proposed COVID-RDNet model is shown in Fig. 9 and its innovations are as follows:
Multichannel image contrast enhancement based on linguistic rule-based intensificators
2019, Applied Soft Computing JournalCitation Excerpt :Many other techniques for image enhancement are exploited, e.g. global histogram equalisation [39], logarithmic transform histogram shifting and histogram-based image enhancement [40], dynamic histogram equalisation technique [41], multi-histogram normalisation method [42], and discrete cosine transform [43]. However, only few studies follow the direct approach that modify the image contrast at each pixel of the image, e.g. [21,22,38,44,45]. In [38,44], it is proved that the direct method offers techniques that can produce more effective results.
Automatic wavelet base selection and its application to contrast enhancement
2010, Signal ProcessingCOVID-RDNet: A novel coronavirus pneumonia classification model by the mixed dataset
2022, Research Square