In this research, we present a methodology for extracting very narrow lines in correlated noisy environments. The approach is a generalization of the analysis of variance applied to the symmetrically balanced incomplete-blocks design. It encompasses many well-known algorithms when subjected to more restrictive conditions. The detector is robust and superior to the polynomial-approximation-based detector and the classical Prewitt detector. The procedure detects narrow lines embedded in nonuniform background without compromising resolution, and performs satisfactorily in severe corruptive noise. Extensive computer simulations demonstrate the practicality of the detector on real imaging environments.

In this paper, the problem of detecting narrow lines embedded in correlated noise is investigated. An approach based on the contrast theory is adapted to the four-way Graeco-Latin squares design. The analysis provides a theoretical basis for the comparison of a line detector to other detectors that ignore noise dependence. A new and compensated algorithm is developed for detecting lines oriented in any major direction in a two-dimensional image plane. The four-way design is also shown to remove any background obscuration, and the actual processor is robust, simple, efficient, and suitable for real-time applications. Extensive computer simulations demonstrate the performance of the proposed detector relative to some classical mask-based detectors.

In this paper, the classical analysis of variance is extended to three-dimensional (3-D) Graeco-Latin squares design for multiframe processing applications. Conspicuous physical features, including edges, lines, and corners, can then be expressed as contrast functions. This enables the development of a new methodology for detecting moving objects embedded in noise. The new detector exploits spatial and temporal information uniformly most powerful in a Gaussian environment with unknown and time-varying noise variance. Also found is that a moving object detector based on contrast functions coincides with a sufficient statistic of the generalized likelihood ratio test. Extensive image analysis demonstrates the practicality of the detector and compares favorably to other classes of detectors.

A new class of line detectors based on the theory of linear contrasts is developed. By incorporating the F-statistic and the shape test, the new algorithm detects and locates line features simultaneously, and thereby increases the detection power substantially. Many classes of detectors, including the step edge detector, naturally becomes subclasses of the proposed detector. Mathematical analysis indicates that the new algorithm that accounts for noise dependence performs better than its counterpart that neglects the dependence of noise. A symmetrical balanced incomplete-blocks (SBIB) design is introduced to model real-world images in terms of a treatment group that is associated with preexistent, conspicuous, features such as lines, edges, etc., and an incomplete treatment group that is randomized to accommodate image textures. The new algorithm in conjunction with the SBIB design is applied in the four main directions, perpendicularly to the line under investigation. It is robust, simple, efficient, and amenable to real-time applications. All pertinent statistics for the implementation of the procedure are estimated from data. Extensive computer simulations demonstrate the performance of the proposed detector.

A detector is proposed that is based on a model in which the signal components consist of radiant thermal energy from either the small target or the intense, highly structured background. The resulting statistic is effective in enhancing the target and suppressing cluttered background. Estimation of the system parameters based on stochastic approximation techniques is presented. Simulation results demonstrate the practicality of the proposed detector.