Satellite Image Contrast Research Articles

CONTRAST IMPROVEMENT TECHNIQUE SATELLITE IMAGES APPLYING FILTRATION METHOD

In this paper introduces advanced contrast technology considering. The input images taken from remote Satellites are used in many applications but the images taken are not very enhanced and may contain blurry or less contrast. Despite the growing demand for better remote sensing pictures different methodology was proposed but they are not able to preserve the edge details and Saturation of high and low brightness images areas. Histogram equalization (HE) was the most familiar approach to raising the contrast in various applications. But cannot maintain the shape information and cannot preserve the average Image brightness, which may be lower or higher than the reprocessed image saturation. The suggested algorithm solves this type problem by using effective techniques used for enhanced satellite image contrast using the atomization resolution of atomization of dominant brightness level, ADT function and smoothing of boundary. Experimental results show, that the suggested method rise the contrast and the perspective of the local details that is improved than current techniques and retains poor image information. The advanced approach can definitely improve any depressed contrast images and maintain the edge contingent Purchased with a satellite camera and are also suitable for other imaging devices such as user digital cameras, and compression cameras.

Performance Enhancement of Differential Evolution by Incorporating Lévy Flight and Chaotic Sequence for the Cases of Satellite Images

Differential Evolution (DE) is a simple but powerful evolutionary algorithm. Crossover Rate (CR) and Mutation Factor (F) are the most important control parameters in DE. Mutation factor controls the diversification. In traditional DE algorithm CR and F are kept constant. In this paper, the values of CR and F are modified to enhance the capability of traditional DE algorithm. In the first modified algorithm chaotic sequence is used to perform this modification. In the next modified algorithm Lévy Flight with chaotic step size is used for such enhancement. In the second modified DE, population diversity has been used to build population in every generation. As a result the algorithm does not converge prematurely. Both modified algorithms have been applied to optimize parameters of the parameterized contrast stretching function. The algorithms are tested for satellite image contrast enhancement and the results are compared, which show that DE via chaotic Lévy and population diversity information outperforms the traditional and chaotic DE in the image enhancement domain.

Cuckoo search algorithm based satellite image contrast and brightness enhancement using DWT–SVD

This paper presents a new contrast enhancement approach which is based on Cuckoo Search (CS) algorithm and DWT-SVD for quality improvement of the low contrast satellite images. The input image is decomposed into the four frequency subbands through Discrete Wavelet Transform (DWT), and CS algorithm used to optimize each subband of DWT and then obtains the singular value matrix of the low-low thresholded subband image and finally, it reconstructs the enhanced image by applying IDWT. The singular value matrix employed intensity information of the particular image, and any modification in the singular values changes the intensity of the given image. The experimental results show superiority of the proposed method performance in terms of PSNR, MSE, Mean and Standard Deviation over conventional and state-of-the-art techniques.

Artificial Bee Colony-based satellite image contrast and brightness enhancement technique using DWT-SVD

In this article, a new contrast enhancement approach is presented for quality enhancement of low-contrast satellite images. The proposed technique is based on the Artificial Bee Colony (ABC) algorithm using Discrete Wavelet Transform and Singular Value Decomposition (DWT-SVD). The method employs the ABC technique to learn the parameters of the adaptive thresholding function required for optimum enhancement. In this approach, the input image is primarily decomposed into four sub-bands through DWT, and then each sub-band of DWT is optimized through the ABC algorithm. After that, a singular value matrix of the low–low thresholded sub-band image is estimated and, finally, the enhanced image is constructed by applying inverse DWT. The results obtained through this method reveal that the proposed methodology gives better performance in terms of peak signal-to-noise ratio (PSNR), mean square error (MSE), and mean and standard deviation as compared to General Histogram Equalization (GHE), Discrete Cosine Transform and Singular Value Decomposition (DCT-SVD), DWT-SVD, Particle Swarm Optimization (PSO), and modified versions of the PSO-based enhancement approach.

Low‐contrast satellite images enhancement using discrete cosine transform pyramid and singular value decomposition

This study presents a satellite image contrast enhancement technique based on the discrete cosine transform (DCT) pyramid and singular value decomposition (SVD), in contrast to the methods based on wavelet decomposition and SVD which could fail to produce satisfactory results for some low-contrast images. With the proposed method, an input image is decomposed into a low sub-band image and reversed L-shape blocks containing the high-frequency coefficients of the DCT pyramid. The singular value matrix of the equalised low sub-band image is then estimated from the combination between the singular matrix of the low sub-band image and the singular matrix of its global histogram equalisation. The qualitative and quantitative performances of the proposed technique are compared with those of conventional image equalisation such as general histogram equalisation and local histogram equalisation, as well as some state-of-the-art techniques such as singular value equalisation technique. Moreover, the proposed technique is contrasted against the technique based on the discrete wavelet transform (DWT) and SVD (DWT-SVD) as well as the technique based on DCT-SVD. The experimental results show that the proposed method outperforms both conventional and the state-of-the-art techniques.

DWT and P C a Based Image Enhancement with local Neighborhood filter Mask

In this letter, a new satellite image contrast enhance- ment technique based on the discrete wavelet transform (DWT) and singular value decomposition has been proposed. The tech- nique decomposes the input image into the four frequency sub- bands by using DWT and estimates the singular value matrix of the low-low subband image, and, then, it reconstructs the en- hanced image by applying inverse DWT. The technique is com- pared 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 bright- ness 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.

Image Contrast Enhancement using Atrous Wavelet Transforn and Singular Value Decomposition(SVD)

this paper, a new satellite image contrast enhancement technique based on s Atrous wavelet transform and singular value decomposition (SVD) has been proposed. To obtain shift invariant discrete wavelet transform decomposition for images, we introduced the discrete wavelet transform known a A trous algorithm to decompose the image into wavelet planes which is computed as the difference between two consecutive approximations and calculate the singular value matrix of the residual image. The enhanced image is obtained by applying inverse transforms. These techniques are compared with conventional image equalization techniques such as general histogram equalization and discrete wavelet transform. Quality metrics were used to evaluate the superiority of the proposed method.

Satellite Image Contrast Enhancement Using Discrete Wavelet Transform and Singular Value Decomposition

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.