The range of reflectance values acquired by a sensor may not match the capabilities of the film or color display panel, which is a typical difficulty in remote sensing. Different materials on the Earth's surface reflect and emit varying quantities of energy. A sensor may record a large quantity of energy from one substance in a specific wavelength, whereas another material in the same wavelength records substantially less energy. Image enhancement methods make it simpler to study and comprehend an image. Contrast refers to the range of brightness levels exhibited in a picture. Image Contrast enhancement online is a procedure that increases the visibility of visual characteristics by making the most use of the colors available on the display or output device.
What is image contrast enhancement online
Image enhancement is one of the most interesting and visually appealing areas of image processing. It involves operations such as enhancing contrast, reducing noise for improving the quality of the image. This paper presents an analysis of the mathematical morphological approach with comparison to various other state-of-art techniques for addressing the problems of low contrast in images.
Image Contrast enhancement online is one of the important research issues of image enhancement. There are many image contrast enhancement online methods which have been proposed in the literature. A very popular technique for image enhancement is histogram equalization (HE). This technique is commonly employed for image enhancement because of its simplicity and comparatively better performance on almost all types of images. This technique has certain limitations which are being discussed in the following section. Some researchers have also focused on improvement of histogram equalization based image contrast enhancement online such as adaptive histogram equalization which helps to enhance the contrast locally . Mathematical morphology is a relatively new approach to image processing and analysis. The top hat transformation is used to improve the contrast of the images based on the shape and the size of the structuring element.
Some Image Contrast Enhancement Techniques
Histograms Equalization
This technique is widely used because it is simple and easy to implement. This can be used for image contrast enhancement online of all types of images. It works by flattening the histogram and stretching the dynamic range of the gray levels by using the cumulative density function of the image. The most widely used application areas for histogram equalization is medical field image-processing, radar image-processing etc. The biggest disadvantage of this method is it does not preserve brightness of an image. The brightness get changed after histogram equalization. Hence preserving the original brightness and enhancing contrast are essential to avoid other side effects.
Brightness preserving bi-histogram equalization
In this technique, the input image is decomposed and two sub-images. These two images are formed on the basis of gray level mean value. The drawback introduced by HE method is overcome by this method. Then HE method is applied on each of the sub-images. This method equalizes both the images independently. Their respective histograms with a constraint that samples in the first sub-image are mapped in the range from minimum gray level to input mean and samples in second sub-image are mapped in the range from mean to maximum gray level.
The resultant equalized sub-images are bounded by each other around input mean. The output image produced by BBHE has the value of brightness (mean gray-level) located in the middle of the mean of the input image.
Dualistic Sub-image Histogram Equalization
In this method the original image is divided into two equal area sub-images based on gray level probability density function of input image. The DSIHE technique for image contrast enhancement online decomposes an image into two equal area sub-images, one dark and one bright, following the equal area property. Resulting image of dualistic sub-image histogram equalization (DSIHE) is obtained after the two equalized sub-images will be composed into one image.
Minimum Mean Brightness Error Bi-Histogram Equalization
The basic principle behind this method is that decomposition of image into two sub images and applying equalization process independently to the resulting sub images which is similar to BBHE and DSIHE except difference is that this technique searches for a threshold level lt, which decomposes input image into two sub images in such a way that the minimum brightness difference between the input and the output image is achieved. This is called absolute mean brightness error.
image contrast enhancement online in saiwa
Image processing is the act of altering the look of a photograph in order to boost its aesthetic information for human interpretation or unsupervised computer perception. “Digital image processing” is a subfield of electronics in which a photograph is turned into an array of tiny numbers called pixels that indicate a physical quality such as ambient brightness, stored in digital storage, and processed by a computer or other digital hardware. The appeal of digital imaging techniques stems from two main areas of application: picture enhancement for human interpretation and image data processing for unsupervised machine vision storage, transmission, and display. In this post, we will discuss a variety of online image processing technologies developed and built specifically by Saiwa.
What are the advantages of image enhancement?
Enhancements are used to make it easier for visual interpretation and understanding of imagery. The advantage of digital imagery is that it allows us to manipulate the digital pixel values in an image. Although radiometric corrections for illumination, atmospheric influences, and sensor characteristics may be done prior to distribution of data to the user, the image may still not be optimized for visual interpretation. Remote sensing devices, particularly those operated from satellite platforms, must be designed to cope with levels of target/background energy which are typical of all conditions likely to be encountered in routine use. With large variations in spectral response from a diverse range of targets (e.g. forest, deserts, snowfields, water, etc.) no generic radiometric correction could optimally account for and display the optimum brightness range and contrast for all targets. Thus, for each application and each image, a custom adjustment of the range and distribution of brightness values is usually necessary.
