An objective metric for grayscale image quality capable of both global and localized quality assessment and based on a direct comparison of visual information in the test and reference images is proposed. The measure associates visual information with edge, or gradient, information that is initially extracted at all locations of the test and reference images. A perceptual-information preservation model is then used to quantify the success of information transfer/loss. By considering the perceptual importance of different image regions, local image quality success estimates are integrated into a single, numerical quality score between 0 (total information loss) and 1 (ideal transfer). The proposed metric is validated using extensive subjective test results. Results indicate that even in its basic form the proposed metric is perceptually meaningful and yields performance comparable with state-of-the art objective image quality metrics.

Groupwise image registration algorithms seek to establish dense correspondences between sets of images. Typically, they involve iteratively improving the registration between each image and an evolving mean. A variety of methods have been proposed, which differ in their choice of objective function, representation of deformation field, and optimization methods. Given the complexity of the task, the final accuracy is significantly affected by the choices made for each component. Here, we present a groupwise registration algorithm which can take advantage of the statistics of both the image intensities and the range of shapes across the group to achieve accurate matching. By testing on large sets of images (in both 2D and 3D), we explore the effects of using different image representations and different statistical shape constraints. We demonstrate that careful choice of such representations can lead to significant improvements in overall performance.

We describe an approach for automated analysis of deformable objects which extracts structure information from groups of images containing different examples of the object with a particular application to human imaging. The proposed analysis framework simultaneously segments and registers a set of images, incrementally constructing a model of the composition of the object. By fitting an appropriate intensity distribution model to the image we obtain a soft segmentation which allows us to explicitly model the construction of each pixel from constituent image segments, rather than its expected intensity. This effectively decouples the model from the effects of the imaging system and varying statistics in different examples. When estimating the optimal deformation field for each example, the original image is compared to a reconstruction, generated using the composition model and its intensity distribution parameters for each segment (i.e. an estimate of how the model would appear given the imaging conditions for that image). In the paper we describe the algorithm in detail and show results of applying it to two sets of medical images of different anatomies taken with different imaging modalities. We present quantitative results demonstrating that the proposed algorithm is more powerful than current state of the art methods at extracting structural information such as spatial correspondences across groups of images with varying statistics. 1

This paper deals with the problem of objective evaluation of dynamic, multi-sensor image fusion. For this purpose an established static image fusion evaluation framework, based on gradient information preservation between the inputs and the fused image, is extended to deal with additional scene and object motion information present in multi-sensor sequences. In particular formulations for dynamic, multi-sensor information preservation models are proposed to provide space-time localised fusion performance estimates. Perceptual importance distribution models are derived to accommodate temporal data and provide a natural generalisation of localised performance estimates into both global and continuous dynamic fusion performance scores. The proposed system is described in detail and shown to exhibit better evaluation accuracy, robustness and sensitivity when compared to existing dynamic fusion metrics on an evaluation of several established image fusion algorithms applied to multi- sensor sequences from an array of dynamic fusion scenarios.

The considerable number of image fusion algorithms available today vary widely in terms of fusion performance and robust fusion assessment tools have become a target of considerable research. Based on a variety of localised or global evaluations of image statistics and structure between the inputs and the fused image, available objective fusion evaluation metrics use a number of different information representation and information loss models. This paper explores the definition of an optimal information representation for the evaluation of multisensor image fusion and how it can be defined based on the actual application of fused information. Extensive evaluations of a considerable data set of subjectively annotated fused images with a variety of information representation approaches implemented using three different global fusion evaluation frameworks are presented. The results show that if used with correct information representation models global, statistical approaches can yield significantly better fusion evaluation performance than existing methods.

A plethora of image fusion algorithms have been proposed recently, yet what are optimal fusion parameters that should be used for any multi-sensor dataset cannot be defined a priori. They could be learned by evaluating all available fusion strategies on large, representative datasets, but this is not practical and provides no guarantee that fusion performance will remain optimal should real input conditions differ from sample data. This paper proposes and examines the viability of a powerful framework for objectively optimal image fusion that explicitly optimises fusion performance for any set of input conditions. The idea is to integrate proven concepts used in objective image fusion evaluation metrics to optimally adapt the fusion process to the input conditions. Specific focus is on fusion for display, which has a broad appeal in a wide range of fusion applications as only metrics shown to be subjectively relevant are considered. The results show that the proposed framework achieves a considerable improvement in both the level and robustness of fusion performance for a wide array of multi-sensor images.

We describe an algorithm for obtaining correspondences across a group of images of deformable objects. The approach is to construct a statistical model of appearance which can encode the training images as compactly as possible (a Minimum Description Length framework). Correspondences are defined by piece-wise linear interpolation between a set of control points defined on each image. Given such points a model can be constructed, which can approximate every image in the set. The description length encodes the cost of the model, the parameters and most importantly, the residuals not explained by the model. By modifying the positions of the control points we can optimise the description length, leading to good correspondence. We describe the algorithm in detail and give examples of its application to MR brain images and to faces. We also describe experiments which use a recently-introduced specificity measure to evaluate the performance of different components of the algorithm. 1

Image fusion as a way of combining multiple image signals into a single fused image has in recent years been extensively researched for a variety of multisensor applications. Choosing an optimal fusion approach for each application from the plethora of algorithms available however, remains a largely open issue. A small number of metrics proposed so far provide only a rough, numerical estimate of fusion performance with limited understanding of the relative merits of different fusion schemes. This paper proposes a method for comprehensive, objective, image fusion performance characterisation using a fusion evaluation framework based on gradient information representation. The method provides an in-depth analysis of fusion performance by quantifying: information contributions by each sensor, fusion gain, fusion information loss and fusion artifacts (artificial information created). It is demonstrated on the evaluation of an extensive dataset of multisensor images fused with a wide range of established image fusion algorithms. The results demonstrate and quantify a number of well known issues concerning the performance of these schemes and provide a useful insight into a number of more subtle yet important fusion performance effects not immediately accessible to an observer.

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A novel approach to multiresolution signal-level image fusion is presented for accurately transferring visual information from any number of input image signals, into a single fused image without loss of information or the introduction of distortion. The proposed system uses a "fuse-then-decompose" technique realized through a novel, fusion/decomposition system architecture. In particular, information fusion is performed on a multiresolution gradient map representation domain of image signal information. At each resolution, input images are represented as gradient maps and combined to produce new, fused gradient maps. Fused gradient map signals are processed, using gradient filters derived from high-pass quadrature mirror filters to yield a fused multiresolution pyramid representation. The fused output image is obtained by applying, on the fused pyramid, a reconstruction process that is analogous to that of conventional discrete wavelet transform. This new gradient fusion significantly reduces the amount of distortion artefacts and the loss of contrast information usually observed in fused images obtained from conventional multiresolution fusion schemes. This is because fusion in the gradient map domain significantly improves the reliability of the feature selection and information fusion processes. Fusion performance is evaluated through informal visual inspection and subjective psychometric preference tests, as well as objective fusion performance measurements. Results clearly demonstrate the superiority of this new approach when compared to conventional fusion systems.