Latent topic-based super-resolution for remote sensing

This letter presents a novel single-image Super-Resolution (SR) approach based on latent topics specially designed to remote sensing imagery. The proposed approach pursues to super-resolve topics uncovered from low-resolution images instead of super-resolving image patches themselves. An experimental comparison is con- ducted using nine di ff erent SR methods over four aerial image data- sets. Experiments revealed the potential of latent topics in remote sensing SR by reporting that the proposed approach is able to provide a competitive advantage especially in low noise conditions.This work was supported by the Spanish Ministry of Economy under the projects ESP2013-48458- C4-3-P and ESP2016-79503-C2-2-P, by Generalitat Valenciana through project PROMETEO-II/2014/ 062, and by Universitat Jaume I through project P11B2014-09

Minimal Information Exchange for Image Registration

In this paper we consider the problem of estimating the relative shift, scale and rotation between two images X and Y that are available to two users, respectively A and B, connected through a channel. User A is asked to send B some specifically selected minimal description of image X that will allow B to recover the relative shift, rotation and scale between X and Y. The approach is based on a distributed encoding technique applied to the Discrete Fourier Transform phase and to the Fourier-Mellin transform of the images

Sparse Multi-modal probabilistic Latent Semantic Analysis for Single-Image Super-Resolution

This paper presents a novel single-image super-resolution (SR) approach based on latent topics in order to take advantage of the semantics pervading the topic space when super-resolving images. Image semantics has shown to be useful to relieve the ill-posed nature of the SR problem, however the most accepted clustering-based approach used to define semantic concepts limits the capability of representing complex visual relationships. The proposed approach provides a new probabilistic perspective where the SR process is performed according to the semantics encapsulated by a new topic model, the Sparse Multimodal probabilistic Latent Semantic Analysis (sMpLSA). Firstly, the sMpLSA model is formulated. Subsequently, a new SR framework based on sMpLSA is defined. Finally, an experimental comparison is conducted using seven learningbased SR methods over three different image datasets. Experiments reveal the potential of latent topics in SR by reporting that the proposed approach is able to provide a competitive performance

IMAGE REGISTRATION BY APPLYING THE CEPSTRUM TECHNIQUE TO THE PROJECTIONS

Ötelemesel görüntü çakıştırma probleminin Fourier dönüşümü yaklaşımıyla çözümü geçmişte iki-boyutlu evre ilintisi ve iki-boyutlu kepstrum tekniği ile yapılmıştır. Ancak her iki yöntem de büyük bir hesaplama yüküne sahiptir. Hesaplama yükünün azaltılması için, görüntülerin yerlerine onların tek-boyutlu izdüşümlerine evre ilintisinin uygulanması sonucu daha küçük bir öteleme erimi içinde başarılı sonuçlar alınabilmişti. Benzer bir yolla, kepstrum tekniğinin izdüşümlere uygulanmasına dayalı bir yöntemin geliştirilmesi ve sınanması bu çalışmanın konusudur. Bu yöntem, çakıştırılacak iki görüntünün aynı yöndeki izdüşümlerinin toplamsal kepstrumundan farksal kepstrumunu çıkartarak geliştirilmiştir. Spektrumların logaritma yerine kare kök alarak beyazlaştırılması ve yüksek geçiren süzgeçle şekillendirilmesi sonucu, tek-boyutlu evre ilintisi ile karşılaştırılabilir bir performans elde edilmiştir. Pratikte karşılaşılan gürültü seviyeleri için yöntemin, evre ilintisine göre biraz daha büyük öteleme farkı erimi verdiği görülmüştür. Farklı bulanıklık düzeylerine sahip iki görüntünün çakıştırılmasında gösterdiği üstünlük de deneysel olarak ortaya çıkartılmıştır. Karşılaştırmalı bir deneysel çalışmanın sonuçları ve uygulamaya ilişkin sorunlar sunulmaktadır. Two solutions to the translational image registration problem through the Fourier transform approach were given by two-dimensional phase correlation and two-dimensional cepstrum technique in the past. However, both methods have a large computational load. Applying the phase correlation to the projections of images instead of themselves in order to reduce the computational load, satisfactory results were obtained in a smaller translational range. The subject of this work is first to develop a method based on the application of the cepstrum technique to the projections in a similar way, and then to test the method. The method has been developed by subtracting the differential cepstrum of the projections of the images to be registered from the additive cepstrum. A performance comparable to that of the 1-D phase correlation has been obtained through whitening the spectrums by the square-root instead of the logarithm and shaping by a high-pass filter. It has been seen that the method yields a larger translational range for the levels of noise frequently encountered in practice. It has also been experimentally revealed that the method is superior in registering two images with different levels of blurring. The results of a comparative experimental work and some issues regarding its application are presented

Image Registration Using Redundant Wavelet Transforms

Imagery is collected much faster and in significantly greater quantities today compared to a few years ago. Accurate registration of this imagery is vital for comparing the similarities and differences between multiple images. Since human analysis is tedious and error prone for large data sets, we require an automatic, efficient, robust, and accurate method to register images. Wavelet transforms have proven useful for a variety of signal and image processing tasks, including image registration. In our research, we present a fundamentally new wavelet-based registration algorithm utilizing redundant transforms and a masking process to suppress the adverse effects of noise and improve processing efficiency. The shift-invariant wavelet transform is applied in translation estimation and a new rotation-invariant polar wavelet transform is effectively utilized in rotation estimation. We demonstrate the robustness of these redundant wavelet transforms for the registration of two images (i.e., translating or rotating an input image to a reference image), but extensions to larger data sets are certainly feasible. We compare the registration accuracy of our redundant wavelet transforms to the \u27critically sampled\u27 discrete wavelet transform using the Daubechies (7,9) wavelet to illustrate the power of our algorithm in the presence of significant additive white Gaussian noise and strongly translated or rotated images

Optimizing Techniques and Cramer-Rao Bound for Passive Source Location Estimation

This work is motivated by the problem of locating potential unstable areas in underground potash mines with better accuracy more consistently while introducing minimum extra computational load. It is important for both efficient mine design and safe mining activities, since these unstable areas may experience local, low-intensity earthquakes in the vicinity of an underground mine. The object of this thesis is to present localization algorithms that can deliver the most consistent and accurate estimation results for the application of interest. As the first step towards the goal, three most representative source localization algorithms given in the literature are studied and compared. A one-step energy based grid search (EGS) algorithm is selected to address the needs of the application of interest. The next step is the development of closed-form Cram´er-Rao bound (CRB) expressions. The mathematical derivation presented in this work deals with continuous signals using the Karhunen-Lo`eve (K-L) expansion, which makes the derivation applicable to non-stationary Gaussian noise problems. Explicit closed-form CRB expressions are presented only for stationary Gaussian noise cases using the spectrum representation of the signal and noise though. Using the CRB comparisons, two approaches are proposed to further improve the EGS algorithm. The first approach utilizes the corresponding analytic expression of the error estimation variance (EEV) given in [1] to derive an amplitude weight expression, optimal in terms of minimizing this EEV, for the case of additive Gaussian noise with a common spectrum interpretation across all the sensors. An alternate noniterative amplitude weighting scheme is proposed based on the optimal amplitude weight expression. It achieves the same performance with less calculation compared with the traditional iterative approach. The second approach tries to optimize the EGS algorithm in the frequency domain. An analytic frequency weighted EEV expression is derived using spectrum representation and the stochastic process theory. Based on this EEV expression, an integral equation is established and solved using the calculus of variations technique. The solution corresponds to a filter transfer function that is optimal in the sense that it minimizes this analytic frequency domain EEV. When various parts of the frequency domain EEV expression are ignored during the minimization procedure using Cauchy-Schwarz inequality, several different filter transfer functions result. All of them turn out to be well known classical filters that have been developed in the literature and used to deal with source localization problems. This demonstrates that in terms of minimizing the analytic EEV, they are all suboptimal, not optimal. Monte Carlo simulation is performed and shows that both amplitude and frequency weighting bring obvious improvement over the unweighted EGS estimator

Multi-Modality Imaging: A Software Fusion and Image-Guided Therapy Perspective

With the introduction of computers in medical imaging, which were popularized with the presentation of Hounsfield's ground-breaking work in 1971, numerical image reconstruction and analysis of medical images became a vital part of medical imaging research. While mathematical aspects of reconstruction dominated research in the beginning, a growing body of literature attests to the progress made over the past 30 years in image fusion. This article describes the historical development of non-deformable software-based image co-registration and it's role in the context of hybrid imaging and provides an outlook on future developments

Translation and Rotation Invariant Multiscale Image Registration

The most recent research involved registering images in the presence of translations and rotations using one iteration of the redundant discrete wavelet transform. We extend this work by creating a new multiscale transform to register two images with translation or rotation differences, independent of scale differences between the images. Our two-dimensional multiscale transform uses an innovative combination of lowpass filtering and the continuous wavelet transform to mimic the two-dimensional redundant discrete wavelet transform. This allows us to obtain multiple subbands at various scales while maintaining the desirable properties of the redundant discrete wavelet transform. Whereas the discrete wavelet transform produces results only at dyadic scales, our new multiscale transform produces data at all integer scales. This added flexibility improves registration accuracy without greatly increasing computational complexity and permits accurate registration even in the presence of scale differences