Analysis of displacement errors in high-resolution image reconstruction with multisensors

An image-acquisition system composed of an array of sensors, where each sensor has a subarray of sensing elements of suitable size, has recently been popular for increasing the spatial resolution with high signal-to-noise ratio beyond the performance bound of technologies that constrain the manufacture of imaging devices. Small perturbations around the ideal subpixel locations of the sensing elements (responsible for capturing the sequence of undersampled degraded frames), because of imperfections in fabrication, limit the performance of the signal-processing algorithms for processing and integrating the acquired images for the desired enhanced resolution and quality. The contributions of this paper include an analysis of the displacement errors on the convergence rate of the iterative approach for solving the transform based preconditioned system of equations. Subsequently, it is established that the use of the MAP, L2 norm or H1 norm regularization functional leads to a proof of linear convergence of the conjugate gradient method in terms of the displacement errors caused by the imperfect subpixel locations. Results of simulation support the analytical results.published_or_final_versio

A Study on Super-Resolution Image Reconstruction Techniques

With the rapid development of space technology and its related technologies, more and more remote sensing platforms are sent to outer space to survey our earth. Recognizing and positioning all these space objects is the basis of knowing about the space, but there are no other effective methods in space target recognition except orbit and radio signal recognition. Super-resolution image reconstruction, which is based on the image of space objects, provides an effective way of solving this problem. In this paper, the principle of super-resolution image reconstruction and several typical reconstruction methods were introduced. By comparison, Nonparametric Finite Support Restoration Techniques were analyzed in details. At last, several aspects of super-resolution image reconstruction that should be studied further more were put forward

An Improved Observation Model for Super-Resolution under Affine Motion

Super-resolution (SR) techniques make use of subpixel shifts between frames in an image sequence to yield higher-resolution images. We propose an original observation model devoted to the case of non isometric inter-frame motion as required, for instance, in the context of airborne imaging sensors. First, we describe how the main observation models used in the SR literature deal with motion, and we explain why they are not suited for non isometric motion. Then, we propose an extension of the observation model by Elad and Feuer adapted to affine motion. This model is based on a decomposition of affine transforms into successive shear transforms, each one efficiently implemented by row-by-row or column-by-column 1-D affine transforms. We demonstrate on synthetic and real sequences that our observation model incorporated in a SR reconstruction technique leads to better results in the case of variable scale motions and it provides equivalent results in the case of isometric motions

Preconditioned iterative methods for superresolution image reconstruction with multisensors

We study the problem of reconstructing a super-resolution image f from multiple undersampled, sifted, degraded frames with subpixel displacement errors. The corresponding operator H is a spatially- variant operator. In this paper, we apply the preconditioned conjugate gradient method with cosine transform preconditioners to solve the discrete problems. Preliminary results show that our method converges very fast and gives sound recovery of the super- resolution images.published_or_final_versio

A Review: Enhancement of Degraded Video

We gift Associate in Nursing example-based approach to general improvement of degraded video frames. the tactic depends on building a lexicon with non-degraded elements of the video and to use such a lexicon to boost the degraded elements. The image degradation should originate from a “repeatable” method, in order that the lexicon image patches (blocks) ar equally degraded, so originating a lexicon with degraded blocks and their residues (differences in between degraded and original blocks). Once a match is found between a degraded block within the video and a degraded block within the lexicon, the associated residue of the latter is soft-added to the block of the previous. the tactic could be a generalization of the tactic for example-based super-resolution

A hybrid MLP-PNN architecture for fast image superresolution

Proceedings of Joint International Conference ICANN/ICONIP 2003 Istanbul, Turkey, June 26–29, 2003The final publication is available at Springer via http://dx.doi.org/10.1007/3-540-44989-2_50Image superresolution methods process an input image sequence of a scene to obtain a still image with increased resolution. Classical approaches to this problem involve complex iterative minimization procedures, typically with high computational costs. In this paper is proposed a novel algorithm for superresolution that enables a substantial decrease in computer load. First, a probabilistic neural network architecture is used to perform a scattered-point interpolation of the image sequence data. The network kernel function is optimally determined for this problem by a multi-layer perceptron trained on synthetic data. Network parameters dependence on sequence noise level is quantitatively analyzed. This super-sampled image is spatially filtered to correct finite pixel size effects, to yield the final high-resolution estimate. Results on a real outdoor sequence are presented, showing the quality of the proposed method.This work has been partially supported by TIC2001-0572-C02-02 gran

Aliasing Reduction in Staring Infrared Imagers Utilizing Subpixel Techniques

We introduce and analyze techniques for the reduction of aliased signal energy in a staring infrared imaging system. A standard staring system uses a fixed two-dimensional detector array that corresponds to a fixed spatial sampling frequency determined by the detector pitch or spacing. Aliasing will occur when sampling a scene containing spatial frequencies exceeding half the sampling frequency. This aliasing can significantly degrade the image quality. The aliasing reduction schemes presented here, referred to as microscanning, exploit subpixel shifts between time frames of an image sequence. These multiple images are used to reconstruct a single frame with reduced aliasing. If the shifts are controlled, using a mirror or beam steerer for example, one can obtain a uniformly sampled microscanned image. The reconstruction in this case can be accomplished by a straightforward interlacing of the time frames. If the shifts are uncontrolled, the effective sampling may be nonuniform and reconstruction becomes more complex. A sampling model is developed and the aliased signal energy is analyzed for the microscanning techniques. Finally, a number of experimental results are presented that illustrate the perlormance of the microscanning methods

A total variation regularization based super-resolution reconstruction algorithm for digital video

Super-resolution (SR) reconstruction technique is capable of producing a high-resolution image from a sequence of low-resolution images. In this paper, we study an efficient SR algorithm for digital video. To effectively deal with the intractable problems in SR video reconstruction, such as inevitable motion estimation errors, noise, blurring, missing regions, and compression artifacts, the total variation (TV) regularization is employed in the reconstruction model. We use the fixed-point iteration method and preconditioning techniques to efficiently solve the associated nonlinear Euler-Lagrange equations of the corresponding variational problem in SR. The proposed algorithm has been tested in several cases of motion and degradation. It is also compared with the Laplacian regularization-based SR algorithm and other TV-based SR algorithms. Experimental results are presented to illustrate the effectiveness of the proposed algorithm.£.published_or_final_versio