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Book ChapterDOI

Satellite Image Contrast Enhancement Using Fuzzy Termite Colony Optimization

01 Jan 2018-pp 115-144
TL;DR: This work has proposed a Termite Colony Optimization (TCO) algorithm based on the behavior of termites and uses the proposed algorithm and fuzzy entropy for satellite image contrast enhancement.
Abstract: Image enhancement is an essential subdomain of image processing which caters to the enhancement of visual information within an image. Researchers incorporate different bio-inspired methodologies which imitate the behavior of natural species for optimization-based enhancement techniques. Particle Swarm Optimization imitates the behavior of swarms to discover the finest possible solution in the search space. The peculiar nature of ants to accumulate information about the environment by depositing pheromones is adopted by another technique called Ant Colony Optimization. However, termites have both these characteristics common in them. In this work, the authors have proposed a Termite Colony Optimization (TCO) algorithm based on the behavior of termites. Thereafter they use the proposed algorithm and fuzzy entropy for satellite image contrast enhancement. This technique offers better contrast enhancement of images by utilizing a type-2 fuzzy system and TCO. Initially two sub-images from the input image, named lower and upper in the fuzzy domain, are determined by a type-2 fuzzy system. The S-shape membership function is used for fuzzification. Then an objective function such as fuzzy entropy is optimized in terms of TCO and the adaptive parameters are defined which are applied in the proposed enhancement technique. The performance of the proposed method is evaluated and compared with a number of optimization-based enhancement methods using several test images with several statistical metrics. Moreover, the execution time of TCO is evaluated to find its applicability in real time. Better experimental results over the conventional optimization based enhancement techniques demonstrate the superiority of our proposed methodology.
References
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Journal ArticleDOI
TL;DR: In this letter, a new satellite image contrast enhancement technique based on the discrete wavelet transform (DWT) and singular value decomposition has been proposed and it reconstructs the enhanced image by applying inverse DWT.
Abstract: In this letter, a new satellite image contrast enhancement technique based on the discrete wavelet transform (DWT) and singular value decomposition has been proposed. The technique decomposes the input image into the four frequency subbands by using DWT and estimates the singular value matrix of the low-low subband image, and, then, it reconstructs the enhanced image by applying inverse DWT. The technique is compared with conventional image equalization techniques such as standard general histogram equalization and local histogram equalization, as well as state-of-the-art techniques such as brightness preserving dynamic histogram equalization and singular value equalization. The experimental results show the superiority of the proposed method over conventional and state-of-the-art techniques.

310 citations

Journal ArticleDOI
TL;DR: A new satellite image resolution enhancement technique based on the interpolation of the high-frequency subbands obtained by discrete wavelet transform and the input image to achieve a sharper image is proposed.
Abstract: Satellite images are being used in many fields of research. One of the major issues of these types of images is their resolution. In this paper, we propose a new satellite image resolution enhancement technique based on the interpolation of the high-frequency subbands obtained by discrete wavelet transform (DWT) and the input image. The proposed resolution enhancement technique uses DWT to decompose the input image into different subbands. Then, the high-frequency subband images and the input low-resolution image have been interpolated, followed by combining all these images to generate a new resolution-enhanced image by using inverse DWT. In order to achieve a sharper image, an intermediate stage for estimating the high-frequency subbands has been proposed. The proposed technique has been tested on satellite benchmark images. The quantitative (peak signal-to-noise ratio and root mean square error) and visual results show the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques.

246 citations

Journal ArticleDOI
TL;DR: The experimental results show that the proposed dynamic histogram specification (DHS) algorithm not only keeps the original histogram shape features but also enhances the contrast effectively.
Abstract: A novel contrast enhancement algorithm is proposed The proposed approach enhances the contrast without losing the original histogram characteristics, which is based on the histogram specification technique It is expected to eliminate the annoying side effects effectively by using the differential information from the input histogram The experimental results show that the proposed dynamic histogram specification (DHS) algorithm not only keeps the original histogram shape features but also enhances the contrast effectively Moreover, the DHS algorithm can be applied by simple hardware and processed in real-time system due to its simplicity

192 citations

Journal ArticleDOI
TL;DR: A novel adaptive direct fuzzy contrast enhancement method based on the fuzzy entropy principle and fuzzy set theory is proposed that is very effective in contrast enhancement as well as in preventing over-enhancement.

175 citations

Journal ArticleDOI
TL;DR: A new approach is presented for the enhancement of color images using the fuzzy logic technique and is found to be better than the genetic algorithm (GA)-based and entropy-based approaches.
Abstract: A new approach is presented for the enhancement of color images using the fuzzy logic technique. An objective measure called exposure has been defined to provide an estimate of the underexposed and overexposed regions in the image. This measure serves as the dividing line between the underexposed and overexposed regions of the image. The hue, saturation, and intensity (HSV) color space is employed for the process of enhancement, where the hue component is preserved to keep the original color composition intact. A parametric sigmoid function is used for the enhancement of the luminance component of the underexposed image. A power-law operator is used to improve the overexposed region of the image, and the saturation component of HSV is changed through another power-law operator to recover the lost information in the overexposed region. Objective measures like fuzzy contrast and contrast and visual factors are defined to make the operators adaptive to the image characteristics. Entropy and the visual factors are involved in the objective function, which is optimized using the bacterial foraging algorithm to learn the parameters. Gaussian and triangular membership functions (MFs) are chosen for the underexposed and overexposed regions of the image, respectively. Separate MFs and operators for the two regions make the approach universal to all types of contrast degradations. This approach is applicable to a degraded image of mixed type. On comparison, this approach is found to be better than the genetic algorithm (GA)-based and entropy-based approaches.

168 citations