China
Africa
Explore chapters and articles related to this topic
Application of Image Processing and Data in Remote Sensing
Published in Ankur Dumka, Alaknanda Ashok, Parag Verma, Poonam Verma, Advanced Digital Image Processing and Its Applications in Big Data, 2020
Ankur Dumka, Alaknanda Ashok, Parag Verma, Poonam Verma
Histogram: Histogram equalization is a computer image processing technique that is used to improve contrast in images. It stretches out the intensity range of the image, thereby allowing areas of lower local contrast to gain a higher contrast. There are two types of Histogram Equalizations:Adaptive Histogram Equalization: Adaptive Histogram Equalization computes many histograms where each histogram corresponds to a distinct part of the image, thereby making it suitable for enhancing the definitions of the edges in each region of an image.Contrastive Limited Adaptive Equalization: Contrastive Limited Adaptive Equalization has a transformation function derived from the contrast limiting procedure applied to each neighboring pixel of an image.
An Enhanced Image Dehazing Procedure Using CLAHE and a Guided Filter
Published in Himansu Das, Jitendra Kumar Rout, Suresh Chandra Moharana, Nilanjan Dey, Applied Intelligent Decision Making in Machine Learning, 2020
In this chapter, an efficient single image-based dehazing method is proposed, using Contrast Limited Adaptive Histogram Equalization (CLAHE) and Guided Filter (GF) to remove haze from a hazy image. Histogram Equalization is one of the most commonly used image enhancement techniques, which can only perform well if there is even pixel distribution in the image. Adaptive Histogram Equalization (AHE) can overcome this problem. However, again, the concern with this method is that it tends to over-amplify the contrast of the image. Therefore, CLAHE, a variant of AHE, is introduced. The process is similar to AHE but uses a normalized clip limit of range [0, 1] to reduce over-amplification of noise and contrast. Although it can reduce the problem of noise over-amplification, some information may still be lost because of clipping the histogram. In order to get back the lost information from the CLAHE output image, an edge-preserving GF is applied to the resultant image.
Preprocessing in the Spatial Domain
Published in Sing-Tze Bow, Pattern Recognition and Image Preprocessing, 2002
Use any one of the images given in Appendix A to alter the data by processing each pixel in the image with the deterministic gray-level transformation shown in Figure P12.6, where 0 = dark and 255 = white. Use the altered data obtained as an example image for enhancement processing. Obtain a histogram of the example image.Obtain a processed image by applying the histogram equalization algorithm to these example image data. Evaluate the histogram equalization algorithm by comparing the example image with the image after histogram equalization.Suggest a histogram specification transformation and see whether this can further improve the image.
A Self-Adaptive Chimp-Driven Modified Deep Learning Framework for Autonomous Vehicles to Obtain Autonomous Object Classification
Published in Electric Power Components and Systems, 2023
The mapping function derived from the CDF has the effect of stretching the intensity values of the image. This stretching is done such that the intensity values with high pixel occurrences are spread out over a larger range, resulting in a higher contrast image. Conversely, the intensity values with low pixel occurrences are compressed into a smaller range, resulting in an image with increased detail. Histogram equalization is a simple and effective technique for enhancing the contrast of digital images. It is widely used in image processing applications, including medical imaging, remote sensing, and computer vision. However, the technique has some limitations, such as the over-amplification of noise and artifacts in the image. These limitations have led to the development of more advanced techniques, such as adaptive histogram equalization and contrast-limited adaptive histogram equalization.
Image Enhancement and Implementation of CLAHE Algorithm and Bilinear Interpolation
Published in Cybernetics and Systems, 2022
Venkatesh S., John De Britto C., Subhashini P., Somasundaram K.
Different approaches for low illumination enhancement include methods based on histogram equalization, (Ferguson et al. 2008), noise removal using wiener filter, linear contrast adjustment, CLAHE method and other existing methods. The proposed work is based on image contrast, color enhancement using adaptive gamma correction and histogram equalization. Histogram equalization is used to enhance the digital images, but in most cases it results in over illumination and intensity saturation. The work is based on performing gamma correction adaptively and histogram distribution method for contrast improvement. The gamma correction can be performed adaptively to adjust and enhance the contrast. The weighted histogram distribution presented in the work preserves the color and provides the fine details.
Fog removal and enhancement method for UAV aerial images based on dark channel prior
Published in Journal of Control and Decision, 2023
Fei Xia, Hu Song, Haoxiang Dou
Histogram equalisation method is used to optimise dark channel prior algorithm to remove fog from aerial images and improve image contrast (Li & Krishna, 2021). Histogram Equalisation is an image analysis technique that utilises the histogram to alter the contrast of the image. It distributes out some of the most common pixel intensity values or extends out the image’s intensity values to improve the image’s contrasts (Figure 4).