Network Flow Optimization for Restoration of Images

The network flow optimization approach is offered for restoration of grayscale and color images corrupted by noise. The Ising models are used as a statistical background of the proposed method. The new multiresolution network flow minimum cut algorithm, which is especially efficient in identification of the maximum a posteriori estimates of corrupted images, is presented. The algorithm is able to compute the MAP estimates of large size images and can be used in a concurrent mode. We also describe the efficient solutions of the problem of integer minimization of two energy functions for the Ising models of gray-scale and color images

Image Exploitation-A Forefront Area for UAV Application

Image exploitation, an innovative image utilisation program uses high revisit multisensor, multiresolution imagery from unmanned air vehicle or other reconnaissance platform for intelligent information gathering. This paper describes the imagc exploitation system developed at the Aeronautical Dcvclopment Establishment, Bangalore, for the remotely piloted vehicle (RPV) Nishonr and highlights two major areas (i) In-flight imagc exploitation, and (ii) post-flight imagc cxploitatlon. In-flight imagc study includes real-timeenhancement of images frames during RPV flight. target acquisition. calculation of geo-location of targets, distance and area computation, and image-to-map correspondence. Post-flight image exploitation study includes image restoration, classtfication of terrain, 3-D depth computation using stereo vision and shape from shading techniques. The paper shows results obtained in each of these areas from actual flight trials

Wavelet-based digital image restoration

Digital image restoration is a fundamental image processing problem with underlying physical motivations. A digital imaging system is unable to generate a continuum of ideal pointwise measurements of the input scene. Instead, the acquired digital image is an array of measured values. Generally, algorithms can be developed to remove a significant part of the error associated with these measure image values provided a proper model of the image acquisition system is used as the basis for the algorithm development. The continuous/discrete/continuous (C/D/C) model has proven to be a better alternative compared to the relatively incomplete image acquisition models commonly used in image restoration. Because it is more comprehensive, the C/D/C model offers a basis for developing significantly better restoration filters. The C/D/C model uses Fourier domain techniques to account for system blur at the image formation level, for the potentially important effects of aliasing, for additive noise and for blur at the image reconstruction level.;This dissertation develops a wavelet-based representation for the C/D/C model, including a theoretical treatment of convolution and sampling. This wavelet-based C/D/C model representation is used to formulate the image restoration problem as a generalized least squares problem. The use of wavelets discretizes the image acquisition kernel, and in this way the image restoration problem is also discrete. The generalized least squares problem is solved using the singular value decomposition. Because image restoration is only meaningful in the presence of noise, restoration solutions must deal with the issue of noise amplification. In this dissertation the treatment of noise is addressed with a restoration parameter related to the singular values of the discrete image acquisition kernel. The restoration procedure is assessed using simulated scenes and real scenes with various degrees of smoothness, in the presence of noise. All these scenes are restoration-challenging because they have a considerable amount of spatial detail at small scale. An empirical procedure that provides a good initial guess of the restoration parameter is devised

Rover imaging system for the Mars rover/sample return mission

In the past year, the conceptual design of a panoramic imager for the Mars Environmental Survey (MESUR) Pathfinder was finished. A prototype camera was built and its performace in the laboratory was tested. The performance of this camera was excellent. Based on this work, we have recently proposed a small, lightweight, rugged, and highly capable Mars Surface Imager (MSI) instrument for the MESUR Pathfinder mission. A key aspect of our approach to optimization of the MSI design is that we treat image gathering, coding, and restoration as a whole, rather than as separate and independent tasks. Our approach leads to higher image quality, especially in the representation of fine detail with good contrast and clarity, without increasing either the complexity of the camera or the amount of data transmission. We have made significant progress over the past year in both the overall MSI system design and in the detailed design of the MSI optics. We have taken a simple panoramic camera and have upgraded it substantially to become a prototype of the MSI flight instrument. The most recent version of the camera utilizes miniature wide-angle optics that image directly onto a 3-color, 2096-element CCD line array. There are several data-taking modes, providing resolution as high as 0.3 mrad/pixel. Analysis tasks that were performed or that are underway with the test data from the prototype camera include the following: construction of 3-D models of imaged scenes from stereo data, first for controlled scenes and later for field scenes; and checks on geometric fidelity, including alignment errors, mast vibration, and oscillation in the drive system. We have outlined a number of tasks planned for Fiscal Year '93 in order to prepare us for submission of a flight instrument proposal for MESUR Pathfinder

Combined Industry, Space and Earth Science Data Compression Workshop

The sixth annual Space and Earth Science Data Compression Workshop and the third annual Data Compression Industry Workshop were held as a single combined workshop. The workshop was held April 4, 1996 in Snowbird, Utah in conjunction with the 1996 IEEE Data Compression Conference, which was held at the same location March 31 - April 3, 1996. The Space and Earth Science Data Compression sessions seek to explore opportunities for data compression to enhance the collection, analysis, and retrieval of space and earth science data. Of particular interest is data compression research that is integrated into, or has the potential to be integrated into, a particular space or earth science data information system. Preference is given to data compression research that takes into account the scien- tist's data requirements, and the constraints imposed by the data collection, transmission, distribution and archival systems

VIRTUAL DIVING IN THE UNDERWATER ARCHAEOLOGICAL SITE OF CALA MINNOLA

The paper presents the application of the technologies and methods defined in the VISAS project for the case study of the underwater archaeological site of Cala Minnola located in the island of Levanzo, in the archipelago of the Aegadian Islands (Sicily, Italy). The VISAS project (http://visas-project.eu) aims to improve the responsible and sustainable exploitation of the Underwater Cultural Heritage by means the development of new methods and technologies including an innovative virtual tour of the submerged archaeological sites. In particular, the paper describes the 3D reconstruction of the underwater archaeological site of Cala Minnola and focus on the development of the virtual scene for its visualization and exploitation. The virtual dive of the underwater archaeological site allows users to live a recreational and educational experience by receiving historical, archaeological and biological information about the submerged exhibits, the flora and fauna of the place

A SURE Approach for Digital Signal/Image Deconvolution Problems

In this paper, we are interested in the classical problem of restoring data degraded by a convolution and the addition of a white Gaussian noise. The originality of the proposed approach is two-fold. Firstly, we formulate the restoration problem as a nonlinear estimation problem leading to the minimization of a criterion derived from Stein's unbiased quadratic risk estimate. Secondly, the deconvolution procedure is performed using any analysis and synthesis frames that can be overcomplete or not. New theoretical results concerning the calculation of the variance of the Stein's risk estimate are also provided in this work. Simulations carried out on natural images show the good performance of our method w.r.t. conventional wavelet-based restoration methods

Multi-Scale Edge Detection Algorithms and Their Information-Theoretic Analysis in the Context of Visual Communication

The unrealistic assumption that noise can be modeled as independent, additive and uniform can lead to problems when edge detection methods are applied to low signal-to-noise ratio (SNR) images. The main reason for this is because the filter scale and the threshold for the gradient are difficult to determine at a regional or local scale when the noise estimate is on a global scale. Therefore, in this dissertation, we attempt to solve these problems by using more than one filter to detect the edges and discarding the global thresholding method in the edge discrimination. The proposed multi-scale edge detection algorithms utilize the multi-scale description to detect and localize edges. Furthermore, instead of using the single default global threshold, a local dynamic threshold is introduced to discriminate between edges and non-edges. The proposed algorithms also perform connectivity analysis on edge maps to ensure that small, disconnected edges are removed. Experiments where the methods are applied to a sequence of images of the same scene with different SNRs show the methods to be robust to noise. Additionally, a new noise reduction algorithm based on the multi-scale edge analysis is proposed. In general, an edge—high frequency information in an image—would be filtered or suppressed after image smoothing. With the help of multi-scale edge detection algorithms, the overall edge structure of the original image could be preserved when only the isolated edge information that represents noise gets filtered out. Experimental results show that this method is robust to high levels of noise, correctly preserving the edges. We also propose a new method for evaluating the performance of edge detection algorithms. It is based on information-theoretic analysis of the edge detection algorithms in the context of an end-to-end visual communication channel. We use the information between the scene and the output of the edge-detection algorithm, ala Shannon, to evaluate the performance. An edge detection algorithm is considered to have high performance only if the information rate from the scene to the edge approaches the maximum possible. Therefore, this information-theoretic analysis becomes a new method to allow comparison between different edge detection operators for a given end-to-end image processing system