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Visual image enhancement algorithms predominantly focus on enhancing high spatial frequency components for augmented low-vision aid.
Each noise type was tested with 13 speech enhancement algorithms plus the noisy non-enhanced speech at two SNR levels (5 and 10 dB).
There are some image enhancement algorithms that can adjust the intensity and enhance the contrast at the same time.
Naturally, different speech enhancement algorithms will have different trade-offs.
Over 2 decades of research, many edge enhancement algorithms have been proposed [2].
Conventional enhancement algorithms often produce artifacts that affect the visual quality of the restored images.
For the calculation of the gain, different speech enhancement algorithms can be used.
To sum up, those image enhancement algorithms discussed above have certain deficiencies: 1.
In [18], a statistical method is used to compare the performance of different enhancement algorithms.
Papadopoulos et al. [4] compared five image enhancement algorithms for improving MC cluster detection in mammography.
Most Retinex based enhancement algorithms estimate the reflectance component as the final result.
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