In this paper, we propose a new super resolution (SR) method called the maximum a posteriori based on a universal Hidden Markov Tree (HMT) model for remote sensing images. The HMT theory is first used to set up a prior model for reconstructing super resolved images from a sequence of warped, blurred, subsampled, and noise-contaminated low-resolution (LR) images. Because the wavelet coefficients of images can be well characterized as a mixed Gaussian distribution, an HMT model is better able to capture the dependences between multiscale wavelet coefficients. The new method is tested first against simulated LR views from a single Landsat7 panchromatic scene and, then, with actual data from four Landsat7 panchromatic images captured on different dates. Both tests show that our method achieves better SR results both visually and quantitatively than other methods.

In this letter, the application of superresolution (SR) techniques to multispectral image clustering and classification is investigated and tested using satellite data. A set of multispectral images with better spatial resolution is obtained after an SR technique is applied to several data sets recorded within a short period over a study area. Improved clustering and classification performance is demonstrated visually and quantitatively by comparison with the original low-resolution data or enlarged images using a conventional interpolation method. This letter illustrates the possibility and feasibility of the use of SR reconstruction for the classification of remote sensing data, which is encouraging as a means of breaking through current satellite detectors' resolution limits.

In this paper, we propose a new super resolution method Maximum a Posteriori based on a universal hidden Markov tree model (MAP-uHMT) for remote sensing images. The hidden Markov tree theory in the wavelet domain is used to set up a prior model for reconstructing super resolution images from a sequence of warped, blurred, sub-sampled and contaminated low resolution images. Both the simulation results with a Landsat7 panchromatic image and actual results with four Landsat7 panchromatic images which were captured on different dates show that our method achieves better super resolution images both visually and quantitatively than other methods, based on PSNR in the simulation and derived PSF with actual data.

Reconstruction of an underwater object from a sequence of images distorted by moving water waves is a challenging task. A new approach is presented in this paper. We make use of the bispectrum technique to analyze the raw image sequences and recover the phase information of the true object. We test our approach on both simulated and real-world data, separately. Results show that our algorithm is very promising. Such technique has wide applications to areas such as ocean study and submarine observation.

Long-distance surveillance is an emerging technology which attracts more and more attention from researchers in different areas, particularly for security applications. One of the major challenges of such technology is how to remove the effect due to atmospheric turbulence to get highresolution images. A common practice is to take shortexposure images of the target so that the effect of atmospheric turbulence is frozen, and then the raw images are post-processed to estimate a good quality image (speckle imaging techniques). A new lucky region approach based on such techniques is presented in this paper. The lucky regions are extracted through bicoherence analysis, and the final output image is obtained by post-processing (dewarping and blind deconvolving) the lucky regions. The new technique allows for automatic identification and extraction of lucky regions, and can be easily implemented. Realworld results show that the algorithm is promising.

This paper presents a self-organizing neural network approach, known as multiple self-organizing maps (MSOMs), to multisource data fusion and compound classification. The authors use the Kohonen SOM as a building block to set up a design framework for a range of classifiers. They demonstrate that the MSOM is suitable for multisource fusion, where the issues of high dimensionality, complex characteristics and disparity, and joint exploration of spatiality and temporality of mixed data can be adequately addressed. Experiments with a bitemporal data set show the effectiveness of their approach.

A novel method for visualising atmospheric turbulence is described, based on a point-by-point registration to subpixular accuracy of multiple telescopic images of scenes distorted by the atmosphere, both astronomical and terrestrial. The registration, which produces detailed x and y shift-maps over the space of each image in the sequence, is based on hierarchical, windowed area-correlation. Each x and y shift-map is then used to dewarp the corresponding raw image in the sequence. Registration is made to a prototype average image, which is initially a motion-blurred average of the uncorrected image sequence, but which is later refined to be the average of the dewarped sequence. The process is repented iteratively, using the current best average result as a new prototype. Which represents a motion-blur compensated (restored) image of the scene. The resulting x and y shift-map sequence, viewed as a movie, provides a striking visualisation of the wavefront distortion due to atmospheric turbulence.

High-fidelity image warping preserves image intensity information accurately across a warp. It is important if warping is to be used transparently within general image processing applications, particularly when these involve long sequences of multiple warps such as in morphing. To achieve this aim we require near-perfect interpolation and the avoidance of aliasing. For fast parallel implementation we make use of multipass (scan-line) algorithms, which introduce additional problems. We refer to our previous work on FFT interpolation and ways to avoid aliasing in multi-pass rotation which help it to be a preferred choice for high fidelity work. We show how to visualise aliasing in warping, in practice, for both single-stage and multipass methods, using an artificial test image formed as a "spider web" pattern in the Fourier domain.

The two-pass (or multipass) image geometric transformation algorithm is ideally suited to real-time, parallel implementation, but is known to introduce frequency aliasing during rotation, over and above any aliasing which may result from the usual one-pass algorithm. We develop a unified framework and theory that precisely explains this added-aliasing for many of the well-known multipass algorithms, and show that it is usually less than might be expected at first sight. In some cases, the aliasing occurs in nondestructive, and therefore, theoretically recoverable, forms. We also show that the aliasing is very easily reduced, or avoided altogether, while commenting that this problem should be considered as a special case of a general alias-avoidance strategy in geometric transformation. Finally, we include some examples of multipass image rotations which seem to confirm our predictions.

An array may be reordered according to a common permutation of the digits of each of its element indices. The digit-reversed reordering which results from common fast Fourier transform (FFT) algorithms is an example. By examination of this class of permutation in detail, very efficient algorithms for transforming very long arrays are developed.

A fast method of generating bit-reversed addresses for the fast Fourier transform is described.