Sentence examples for many enhancement algorithms from inspiring English sources

In the existing researches, many enhancement algorithms took the selection of the two parameters of b and c as key factors to improve the non-linear enhancement effect.

However, in spite of the availability of many enhancement algorithms, there have been very little work focusing specifically on dark image (image mean, μ<0.5 [1], where the darkness is due to low illumination, not for dark-colored objects) enhancement, and these methods might also result in over-enhancement and/or unnatural effects.

Over 2 decades of research, many edge enhancement algorithms have been proposed [2].

In many speech enhancement algorithms, such as Wiener filtering, prior knowledge of the noise statistics is a prerequisite for successful ambient noise reduction [30, 38].

We compare PNAE with NDNE and SDRCLCE both quantitatively and visually with LIPC and HIPC images because these two popular algorithms are more effective than many commonly used enhancement algorithms for LIPC and HIPC images and the facts were shown in [1, 6].

There have also been many other single image visibility enhancement algorithms proposed lately, such as [13 15].

Additionally, contrast enhancement algorithms in the DCT domain have attracted many researchers since DCT is adopted in the JPEG compression standard.

Ipv6 brings many enhancement.

Many reasons have contributed to obtain low-contrast CT images, such as using a low-radiation dose during the examination [8], different acquisitions, transmission, storage, display devices, and varied kinds of reconstruction and enhancement algorithms [9].

The above discussion indicates that despite many efforts, intensity shift and over-enhancement are still drawbacks of many enhancement methods.

Naturally, different speech enhancement algorithms will have different trade-offs.