Restoring halftoned color-quantized images with simulated annealing

Centre for Multimedia Signal Processing, Department of Electronic and Information EngineeringRefereed conference paper2003-2004 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Restoration of halftoned color-quantized images using linear estimator

Centre for Multimedia Signal Processing, Department of Electronic and Information EngineeringRefereed conference paper2006-2007 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Restoration of halftoned color-quantized images using projection onto convex sets

Centre for Multimedia Signal Processing, Department of Electronic and Information EngineeringRefereed conference paper2004-2005 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

FAST IMPLEMENTATION TECHNIQUES OF MULTICHANNEL DIGITAL FILTERS FOR COLOR IMAGE PROCESSING USING MATRIX DECOMPOSITIONS

For the processing of color images, multivariable 3-input, 3-output 2-D digital filters are used, considering decomposition in the R, G and B components. Assuming that the three image components are decorrelated, three independent single-input, single-output (SISO) two-dimensional (2-D) digital filters are needed for the processing of each monochromatic image. Additional processing is needed for the correlated noise components in each chan- nel. The requirement of very fast processing dictates the use of special purpose hardware implementations. The VLSI array processors, which are special purpose, locally intercon- nected computing networks, are ideally suited for the fast implementation of digital filters, since they maximize concurrency by exploiting both parallelism and pipelining. In this paper fast implementation architectures of 3-input, 3-output 2-D multi-input digital filters for color image processing that are based on matrix decompositions are presented. The resulting structures are modular, regular, have high inherent parallelism and are easily pipelined, so that they may be implemented via VLSI array processors

COLOR IMAGE IDENTIFICATION AND RESTORATION

Image identification involves estimating the properties of an imperfect imaging system from an observed image prior to the restoration process. In this paper we present a novel identification technique for multichannel image processing using the maximum likelihood estimation (ML) approach. The image is represented as an autoregressive (AR) model and blur is described as a continuous spatial domain model, to overcome some major limitations encountered in other ML methods. Cross-spectral and spatial components, which are inherent to multichannel imaging systems, are also incorporated in the model to improve the overall performance. Also, it is shown that blur extent can be optimally identified from noisy color images that are degraded by uniform linear motion or out-of-focus blurs. The novelty of this approach in identifying the blur of multichannel images is a major contribution in producing visually acceptable results which is significant for higher processing levels

An efficient parallel algorithm for high resolution color image reconstruction

This paper studies the application of preconditioned conjugate gradient methods in high resolution color image reconstruction problems. The high resolution color images are reconstructed from multiple undersampled, shifted, degraded color frames with subpixel displacements. The resulting degradation matrices are spatially variant. The preconditioners are derived by taking the cosine transform approximation of the degradation matrices. The resulting preconditioning matrices allow the use of fast transform methods. We show how the methods can be implemented on parallel computers, and we demonstrate their parallel efficiency using experiments on a sixteen processor IBM SP-2.published_or_final_versio

Computational Imaging Approach to Recovery of Target Coordinates Using Orbital Sensor Data

This dissertation addresses the components necessary for simulation of an image-based recovery of the position of a target using orbital image sensors. Each component is considered in detail, focusing on the effect that design choices and system parameters have on the accuracy of the position estimate. Changes in sensor resolution, varying amounts of blur, differences in image noise level, selection of algorithms used for each component, and lag introduced by excessive processing time all contribute to the accuracy of the result regarding recovery of target coordinates using orbital sensor data. Using physical targets and sensors in this scenario would be cost-prohibitive in the exploratory setting posed, therefore a simulated target path is generated using Bezier curves which approximate representative paths followed by the targets of interest. Orbital trajectories for the sensors are designed on an elliptical model representative of the motion of physical orbital sensors. Images from each sensor are simulated based on the position and orientation of the sensor, the position of the target, and the imaging parameters selected for the experiment (resolution, noise level, blur level, etc.). Post-processing of the simulated imagery seeks to reduce noise and blur and increase resolution. The only information available for calculating the target position by a fully implemented system are the sensor position and orientation vectors and the images from each sensor. From these data we develop a reliable method of recovering the target position and analyze the impact on near-realtime processing. We also discuss the influence of adjustments to system components on overall capabilities and address the potential system size, weight, and power requirements from realistic implementation approaches