Jon Yngve Hardeberg

Characterization of trichromatic color cameras by using a new multispectral imaging technique.

We investigate methods for the recovery of reflectance spectra from the responses of trichromatic camera systems and the application of these methods to the problem of camera characterization. The recovery of reflectance from colorimetric data is an ill-posed problem, and a unique solution requires additional constraints. We introduce a novel method for reflectance recovery that finds the smoothest spectrum consistent with both the colorimetric data and a linear model of reflectance. Four multispectral methods were tested using data from a real trichromatic camera system. The new method gave the lowest maximum colorimetric error in terms of camera characterization with test data that were independent of the training data. However, the average colorimetric performances of the four multispectral methods were statistically indistinguishable from each other but were significantly worse than conventional methods for camera characterization such as polynomial transforms.

Attributes of image quality for color prints

The evaluation of perceived image quality in color prints is a complex task due to its subjectivity and dimensionality. The perceived quality of an image is influenced by a number of different quality attributes. It is difficult and complicated to evaluate the influ- ence of all attributes on overall image quality, and their influence on other attributes. Because of this difficulty, the most important at- tributes of a color image should be identified to achieve a more efficient and manageable evaluation of the images quality. Based on a survey of the existing literature and a psychophysical experi- ment, we identify and categorize existing image quality attributes to propose a refined selection of meaningful ones for the evaluation of color prints.

Towards automatic redeye effect removal

The redeye effect is typically formed in amateur photographs taken with a built-in camera flash. Analysis of the available techniques and products indicates that their efficiency in correcting this artifact is limited and their performance is inconsistent. In this work we propose a user friendly solution, which could be used to restore amateur photographs. In the proposed method the redeye effect is detected using a skin detection module and eye colors are restored using morphological image processing. The new method is computationally efficient, robust to parameter settings and versatile, as it can work in conjunction with a number of skin detection methods.

Full-Reference Image Quality Metrics: Classification and Evaluation

The wide variety of distortions that images are subject to during acquisition, processing, storage, and reproduction can degrade their perceived quality. Since subjective evaluation is time-consuming, expensive, and resource-intensive, objective methods of evaluation have been proposed. One type of these methods, image quality (IQ) metrics, have become very popular and new metrics are proposed continuously. This paper aims to give a survey of one class of metrics, full-reference IQ metrics. First, these IQ metrics were classified into different groups. Second, further IQ metrics from each group were selected and evaluated against six state-of-the-art IQ databases.

Measuring perceptual contrast in digital images

In this paper we present a novel method to measure perceptual contrast in digital images. We start from a previous measure of contrast developed by Rizzi et al. [26], which presents a multilevel analysis. In the first part of the work the study is aimed mainly at investigating the contribution of the chromatic channels and whether a more complex neighborhood calculation can improve this previous measure of contrast. Following this, we analyze in detail the contribution of each level developing a weighted multilevel framework. Finally, we perform an investigation of Regions-of-Interest in combination with our measure of contrast. In order to evaluate the performance of our approach, we have carried out a psychophysical experiment in a controlled environment and performed extensive statistical tests. Results show an improvement in correlation between measured contrast and observers perceived contrast when the variance of the three color channels separately is used as weighting parameters for local contrast maps. Using Regions-of-Interest as weighting maps does not improve the ability of contrast measures to predict perceived contrast in digital images. This suggests that Regions-of-Interest cannot be used to improve contrast measures, as contrast is an intrinsic factor and it is judged by the global impression of the image. This indicates that further work on contrast measures should account for the global impression of the image while preserving the local information.

Optical calibration of a multispectral imaging system based on interference filters

We present a new approach to optically calibrate a multispectral imaging system based on interference filters. Such a system typically suffers from some blurring of its channel images. Because the effectiveness of spectrum reconstruction depends heavily on the quality of the acquired channel images, and because this blurring negatively affects them, a method for deblurring and denoising them is required. The blur is modeled as a uniform intensity distribution within a circular disk. It allows us to characterize, quantitatively, the degradation for each channel image. In terms of global reduction of the blur, it consists of the choice of the best channel for the focus adjustment according to minimal corrections applied to the other channels. Then, for a given acquisition, the restoration can be performed with the computed parameters using adapted Wiener filtering. This process of optical calibration is evaluated on real images and shows large improvements, especially when the scene is detailed.

Spectral recovery using polynomial models

In this paper we apply polynomial models to the problem of reflectance recovery for both three-channel and multispectral imaging systems. The results suggest that the technique is superior in terms of accuracy to a standard linear transform and its generalisation performance is equivalent provided that some regularisation is employed. The experiments with the multispectral system suggest that this advantage is reduced when the number of sensors are increased.

Spatial Arrangement of Color Filter Array for Multispectral Image Acquisition

In the past few years there has been a significant volume of research work carried out in the field of multispectral image acquisition. The focus of most of these has been to facilitate a type of multispectral image acquisition systems that usually requires multiple subsequent shots (e.g. systems based on filter wheels, liquid crystal tunable filters, or active lighting). Recently, an alternative approach for one-shot multispectral image acquisition has been proposed; based on an extension of the color filter array (CFA) standard to produce more than three channels. We can thus introduce the concept of multispectral color filter array (MCFA). But this field has not been much explored, particularly little focus has been given in developing systems which focuses on the reconstruction of scene spectral reflectance. In this paper, we have explored how the spatial arrangement of multispectral color filter array affects the acquisition accuracy with the construction of MCFAs of different sizes. We have simulated acquisitions of several spectral scenes using different number of filters/channels, and compared the results with those obtained by the conventional regular MCFA arrangement, evaluating the precision of the reconstructed scene spectral reflectance in terms of spectral RMS error, and colorimetric ▵E*ab color differences. It has been found that the precision and the the quality of the reconstructed images are significantly influenced by the spatial arrangement of the MCFA and the effect will be more and more prominent with the increase in the number of channels. We believe that MCFA-based systems can be a viable alternative for affordable acquisition of multispectral color images, in particular for applications where spatial resolution can be traded off for spectral resolution. We have shown that the spatial arrangement of the array is an important design issue.

A New Spatial Hue Angle Metric for Perceptual Image Difference

Color image difference metrics have been proposed to find differences between an original image and a modified version of it. One of these metrics is the hue angle algorithm proposed by Hong and Luo in 2002. This metric does not take into account the spatial properties of the human visual system, and could therefore miscalculate the difference between an original image and a modified version of it. Because of this we propose a new color image difference metrics based on the hue angle algorithm that takes into account the spatial properties of the human visual system. The proposed metric, which we have named SHAME (Spatial Hue Angle MEtric), have been subjected to extensive testing. The results show improvement in performance compared to the original metric proposed by Hong and Luo.

Saliency for Spectral Image Analysis

We introduce a new feature extraction model for purposes of image comparison, visualization and interpretation. We define the notion of spectral saliency, as the extent to which a certain group of pixels stands out in an image and in terms of reflectance, rather than in terms of colorimetric attributes as it is the case in traditional saliency studies. The model takes as an input a multi- or hyper-spectral image with any dimensionality, any range of wavelengths, and it uses a series of dedicated feature extractions to output a single saliency map. We also present a local analysis of the image spectrum allowing to produce such maps in color, thus depicting not only which objects are salients, but also in which range of wavelengths. A variety of applications can be derived from the resulting maps, particularly under the scope of visualization, such as the saliency-driven evaluation of dimensionality reduction techniques. Results show that spectral saliency provides valuable information, which do not correlate neither with visual saliency, second-order statistics nor with naturalness, but serve however well for visualization-related applications.