Method of improving the resolution of a moving object in a digital image sequence

Method of improving the resolution of a small moving object in a digital image sequence comprises the steps of: constructing (101) a high-resolution image background model, detecting (102) the moving object using the high-resolution image background model, fitting (103) a model-based trajectory for object registration, and producing (104) a high...

Performance study on point target detection using super-resolution reconstruction

When bright moving objects are viewed with an electro-optical system at very long range, they will appear as small slightly blurred moving points in the recorded image sequence. Detection of point targets is seriously hampered by structure in the background, temporal noise and aliasing artifacts due to undersampling by the infrared (IR) sensor....

Superresolution reconstruction for moving point target detection

Robust super-resolution without regularization

Robust super-resolution by minimizing a Gaussian-weighted L2 error norm

Performance Evaluation of Super-Resolution Reconstruction Methods on Real-World Data

The performance of a super-resolution (SR) reconstruction method on real-world data is not easy to measure, especially as a ground-truth (GT) is often not available. In this paper, a quantitative performance measure is used, based on triangle orientation discrimination (TOD). The TOD measure, simulating a real-observer task, is capable of...

Resolution enhancement of low quality videos using a high-resolution frame

This paper proposes an example-based Super-Resolution (SR) algorithm of compressed videos in the Discrete Cosine Transform (DCT) domain. Input to the system is a Low-Resolution (LR) compressed video together with a High-Resolution (HR) still image of similar content. Using a training set of corresponding LR-HR pairs of image patches from the HR...

Robust Fusion of Irregularly Sampled Data Using Adaptive Normalized Convolution

We present a novel algorithm for image fusion from irregularly sampled data. The method is based on the framework of normalized convolution (NC), in which the local signal is approximated through a projection onto a subspace. The use of polynomial basis functions in this paper makes NC equivalent to a local Taylor series expansion. Unlike the...

Performance of optimal registration estimators

This paper derives a theoretical limit for image registration and presents an iterative estimator that achieves the limit. The variance of any parametric registration is bounded by the Cramer-Rao bound (CRB). This bound is signal-dependent and is proportional to the variance of input noise. Since most available registration techniques are biased...

Influence of signal-to-noise ratio and point spread function on limits of super-resolution

This paper presents a method to predict the limit of possible resolution enhancement given a sequence of lowresolution images. Three important parameters influence the outcome of this limit: the total Point Spread Function (PSF), the Signal-to-Noise Ratio (SNR) and the number of input images. Although a large number of input images captured by a...