A sparse representation based image steganography using Particle Swarm Optimization and wavelet transform

With the growth of information technology, information security is a major concern in the interactive environment, where there is no security for the messages send to and from the receiver. A technology named image steganography has been employed that ensures security to the covert communication and safeguarding the information. Image steganography hides the secret message in any of the recipient images and sends the secret message such that the message is visible only to the sender and the receiver. This paper proposes a method for image steganography using sparse representation, and an algorithm named Particle Swarm Optimization (PSO) algorithm for effective selection of the pixels for the purpose of embedding the secret audio signal in the image. PSO-based pixel selection procedure uses a fitness function that depends on the cost function. Cost function calculates the edge, entropy, and intensity of the pixel for evaluating fitness. Simulation has been done and comparison of the PSO with the other existing methods in terms of Peak-Signal-to-Noise-Ratio (PSNR) and Mean Square Error (MSE) determines the proposed PSO, as an effective method. The proposed method achieved a better PSNR and MSE values of 47.6 dB and 0.75 respectively. Keywords: DWT, Image steganography, IDWT, PSNR, MS

Robust High Resolution Image from the Low Resolution Satellite Image

Abstract — In this paper, we propose a framework detecting and locating the land cover classes from a low-resolution image, which can play a very important role in the satellite surveillance image from the MODIS data. The lands cover classes by constructing superresolution images from the MODIS data. The highest resolution of the MODIS images is 250 meters per pixel. By magnifying and de-blurring the low-resolution satellite image through the kernel regression. SR reconstruction is image interpolation that has been used to increase the size of a single image. The SRKR algorithm takes a single low-resolution image and generates a de-blurred high-resolution image. We perform bi-cubic interpolation on the input low-resolution image (LR) with a desired scaling factor. Finally, the KR model is then used to generate the de-blurred HR image. K-means is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem, which generates a specific number of disjoint, flat (non-hierarchical) clusters. K-means clustering is employ in order to compare MODIS data and recognize land cover type, i.e., “Forest”, “Land”, “sea”, and “Ice”. Index Terms — Satellite LR Image, Super-Resolution Image, MODIS Dat