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

• Published in: International journal of remote sensing Vol. 35; no. 5; pp. 1601 - 1624
• Main Authors: Bhandari, Ashish Kumar, Soni, Vivek, Kumar, Anil, Singh, Girish Kumar
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 04.03.2014
• Subjects: Algorithms; Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Decomposition; Discrete cosine transform; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Image contrast; Image enhancement; Internal geophysics; remote sensing; Satellites; Standard deviation; Swarm intelligence; Teledetection and vegetation maps; wavelet; algorithms; Modification; histograms; adaptation; Satellite observation; decomposition; Method; Image; Particle swarm optimization; Mean square error; quality; Result; Optimum; brightness; techniques; signal-to-noise ratio; Colony; Parameter; Inverse; performances; methodology; Image contrast; standard deviation; Threshold
• ISSN: 0143-1161; 1366-5901; 1366-5901
• DOI: 10.1080/01431161.2013.876518

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.