Lightness enhancement by sigmoid function

In this paper we purposed algorithm, to enhancement the contrast and lightening  of Color image .it is use to solve the problem of low lightening or non-uniform lightening. The purposed algorithm  is called (Lightening Enhancement by Sigmoid Function) " LESF", this algorithm consist of  three parts  the first  Adaptive luminance enhancement second contrast enhancement  and third Color restoration. This algorithm compared with other algorithm like  (A new nonlinear adaptive enhancement) (NNAE), MSR( multi-scale Retinex  ) and Histogram equalization  (HE).when we compared this algorithm by using entropy, time , Mean Squared Error for hue(Mea-H) and Mean Squared Error for saturation(Mea-S)    , we find The result of (LESF) have a good  result and better visual Comparing to the other methods Keywords: Image Enhancement, adaptation sigmoid function  histogram equalization , Illumination enhancement

A Retinex-based Image Enhancement Scheme with Noise Aware Shadow-up Function

This paper proposes a novel image contrast enhancement method based on both a noise aware shadow-up function and Retinex (retina and cortex) decomposition. Under low light conditions, images taken by digital cameras have low contrast in dark or bright regions. This is due to a limited dynamic range that imaging sensors have. For this reason, various contrast enhancement methods have been proposed. Our proposed method can enhance the contrast of images without not only over-enhancement but also noise amplification. In the proposed method, an image is decomposed into illumination layer and reflectance layer based on the retinex theory, and lightness information of the illumination layer is adjusted. A shadow-up function is used for preventing over-enhancement. The proposed mapping function, designed by using a noise aware histogram, allows not only to enhance contrast of dark region, but also to avoid amplifying noise, even under strong noise environments.Comment: To appear in IWAIT-IFMIA 201

Contrast enhancement using grey scale transformation techniques

The object of this thesis has been to examine grey scale transformation techniques in order to incorporate them into a system for automatically selecting a technique to enhance the contrast in a given image. In order to include existing techniques in the system it was necessary to examine each in detail, and to understand under what conditions it gave good results. It was found that a number of techniques had only a limited scope or suffered from some problem in its design. This led to the development of a new technique based on the display capabilities of a monitor; the adaptation of another technique, globed histogram equalisation, to make it applicable to a wider range of images and the modification of the local histogram equalisation algorithm to smooth different sized regions of the image to the same degree. The resultant algorithms, together with those existing in the literature, were included in the system. The system provides an interactive environment for selecting grey scale transformation techniques. The usual method of choosing a contrast enhancement technique is to apply it, look at the result, discard it if the result is not suitable, or if there is a parameter value to be set, modify its value, and try the technique again. Here a more systematic approach is tried using ideas from Knowledge Based Systems and Object Oriented Systems. A model of the way contrast enhancement techniques are selected is encoded into the system and is used with information obtained by analysing the image (either automatic analysis done by the system, or interactive analysis done with the aid of the user) to select the most appropriate techniques. The techniques selected by the system have to fulfil three quite demanding criteria, ensuring that the system is a reliable and useful tool