Ivar Farup

Fairchild has argued that color spaces are of questionable utility for understanding perception. Here, we go through various concepts of mathematical spaces, ranging from abstract topological spaces to Euclidean ones, and discuss how color stimuli, color matches, and color perceptions can be adequately represented by means of some of these spaces. In particular, we argue that the set of color perceptions can be represented as a metric space that is homeomorphic to a neither open nor closed convex cone in an n ‐dimensional Banach space of equivalence classes of “generalized metamers.” Finally, for higher color metrics, we sketch how Riemannian geometry can be used to create a unified representation of color stimuli and color perceptions.

Accurate characterization of goniochromatic materials, which show angle-dependent color variations, is a challenging task due to their complex optical behavior resulting from diffraction, thin-film interference, and structural coloration. The bidirectional reflectance distribution function (BRDF) is an effective tool for characterizing these materials. In this work, we examine a recently proposed empirical goniochromatic BRDF model and assess its performance for our highly specular and highly goniochromatic samples. Using a few measurements obtained from a handheld goniospectrophotometer, we propose a separation between the near-specular and diffuse components. This modification significantly enhances the original model’s performance, enabling more accurate representation and analysis of our goniochromatic materials.

As the number of people affected by diseases in the gastrointestinal system is ever-increasing, a higher demand for preventive screening is inevitable. This will significantly increase the workload on gastroenterologists. To help reduce the workload, tools from computer vision may be helpful. In this paper, we investigate the possibility of constructing 3D models of whole sections of the human colon using image sequences from wireless capsule endoscope video, providing improved visualization as well as position localization of pathologies for gastroenterologists. As capsule endoscope images contain distortion and artifacts non-ideal for many 3D reconstruction algorithms, the problem is challenging. However, recent developments of virtual graphics-based models of the human gastrointestinal system, where distortion and artifacts can be enabled or disabled, make it possible to “dissect” the problem. The graphical model also provides a ground truth, enabling computation of geometric distortion introduced by the 3D reconstruction method. In this paper, most distortions and artifacts are first left out to determine if it is feasible to reconstruct whole sections of the human gastrointestinal system by existing methods. We demonstrate that 3D reconstruction is possible through simultaneous localization and mapping. Further, to reconstruct the gastrointestinal wall surface from resulting point clouds, which vary greatly in density, Poisson surface reconstruction is a good option. Noise and artifacts are then enabled to analyze their effect on the 3D reconstruction. The results are promising, encouraging further research and development on the topic.

Linear minimum mean square error can be used to demosaic images from a color–polarization filter array (CPFA) sensor. Despite its good performance, the reconstruction produces high-frequency artifacts. An additional refinement step could enable enhancement of both the quantitative and visual quality of the demosaiced image. We propose a complete demosaicing framework by first studying the model selection for linear minimum mean square error using cross-validation techniques and then optimizing the anisotropic diffusion parameters. The results show that the training model converges quickly and that the refinement step enables the reduction of the edge artifacts. We also demonstrate that the proposed demosaicing method performs better compared with a dedicated CPFA demosaicing algorithm in terms of peak signal-to-noise ratio.

Anisotropic diffusion has long been an important tool in image processing. More recently, it has also found its way to colour imaging. Until now, mainly Euclidean colour spaces have been considered in this context, but recent years have seen a renewed interest in and importance of non-Euclidean colour geometry. The main contribution of this paper is the derivation of the equations for anisotropic diffusion in Riemannian colour geometry. It is demonstrated that it contains several well-known solutions such as Perona–Malik diffusion and Tschumperlé–Deriche diffusion as special cases. Furthermore, it is shown how it is non-trivially connected to Sochen’s general framework for low-level vision. The main significance of the method is that it decouples the coordinates used for solving the diffusion equation from the ones that define the metric of the colour manifold, and thus directs the magnitude and direction of the diffusion through the diffusion tensor. It also enables the use of non-Euclidean colour manifolds and metrics for applications such as denoising, inpainting, and demosaicing, based on anisotropic diffusion.

This article presents a compact visual colorimeter for the purpose of measuring color-matching functions (CMFs) of individual observers through psychophysical experiments. Constructed from 3D-printed parts, optical elements, and LED-based light engines, the colorimeter facilitates the juxtaposition of two fields to create a bipartite field. The system underwent characterization to evaluate factors that may impact color-matching experiments, such as LED-light stability, spatial homogeneity of the bipartite field, and potential stray-light leakage. The study aimed to assess the accuracy and performance of the system in measuring individual observer CMFs. Results indicate that the system is stable enough to measure both intra- and inter-observer variations in CMFs.

This article presents a compact visual colorimeter for the purpose of measuring colour-matching functions (CMFs) of individual observers through psychophysical experiments. Constructed from 3D-printed parts, optical elements, and LED-based light engines, the colorimeter facilitates the juxtaposition of two fields to create a bipartite field. The system underwent characterisation to evaluate factors that may impact colour-matching experiments, such as LED-light stability, spatial homogeneity of the bipartite field, and potential stray-light leakage. The study aimed to assess the accuracy and performance of the system in measuring individual observer CMFs. Results indicate that the system is stable enough to measure both intra- and inter-observer variations in CMFs.

A wireless capsule endoscope (WCE) is a medical device designed for the examination of the human gastrointestinal (GI) tract. Three-dimensional models based on WCE images can assist in diagnostics by effectively detecting pathology. These 3D models provide gastroenterologists with improved visualization, particularly in areas of specific interest. However, the constraints of WCE, such as lack of controllability, and requiring expensive equipment for operation, which is often unavailable, pose significant challenges when it comes to conducting comprehensive experiments aimed at evaluating the quality of 3D reconstruction from WCE images. In this paper, we employ a single-image-based 3D reconstruction method on an artificial colon captured with an endoscope that behaves like WCE. The shape from shading (SFS) algorithm can reconstruct the 3D shape using a single image. Therefore, it has been employed to reconstruct the 3D shapes of the colon images. The camera of the endoscope has also been subjected to comprehensive geometric and radiometric calibration. Experiments are conducted on well-defined primitive objects to assess the method’s robustness and accuracy. This evaluation involves comparing the reconstructed 3D shapes of primitives with ground truth data, quantified through measurements of root-mean-square error and maximum error. Afterward, the same methodology is applied to recover the geometry of the colon. The results demonstrate that our approach is capable of reconstructing the geometry of the colon captured with a camera with an unknown imaging pipeline and significant noise in the images. The same procedure is applied on WCE images for the purpose of 3D reconstruction. Preliminary results are subsequently generated to illustrate the applicability of our method for reconstructing 3D models from WCE images.

We explored imaging methods to perform in situ field measurements of physical correlates of the visual appearance of snow. Measurements were performed at three locations in Norway between February and March 2023. We used a method to estimate the absorption and scattering coefficients of snow using only one measurement of reflectance captured by the Dia-Stron© TLS850 translucency meter. We also measured the sparkle indicators (contrast and density of sparkle spots) from digital images of snow. The contrast of sparkle spots can be defined as the median value of all the pixels identified as sparkle spots by an algorithm, and the density of sparkle spots is the number of sparkle spots in a selected area of the image. In the case of the sparkle of the snow surface, we found that there is a potential to use the sparkle indicators for classifying the grain types, but it requires a larger data set coupled with expert labelling to define the type of snow. For the absorption and scattering properties, the measurements confirm the fact that snow is a weakly absorptive and highly scattering material when modelling light interactions in the snow. No correlation between the optical properties and sparkle could be found in our data.

Since the introduction of the Retinex theory by Land and McCann in 1971, a multitude of different families, versions, interpretations, implementations, and applications have been proposed. The applications for image enhancement mainly differ in (i) how they explore the locality of the images to determine the local context, and (ii) how they recompute the pixel values based on this context. STRESS (spatio-temporal Retinex-inspired envelopes with stochastic sampling) is one of many quite successful members of the family of Retinex-based image enhancement algorithms. It explores the locality using a stochastic sampling technique, resulting in two envelope images – one maximum and one minimum envelope, completely enclosing the image signal and serving as a representation of the local image context. In this paper, we propose to exchange the stochastic sampling technique of STRESS, which causes significant chromatic noise, with an adapted version of constrained linear anisotropic diffusion for computing the envelopes, resulting in almost noise-free images. Using both subjective experiments and objective image metrics, we show that it improves the perceived and measured image quality and reduces noise artefacts.