An important application of imaging spectroscopy or hyperspectral imaging is the classification or discrimination of pigments based on the obtained spectral reflectance information. As opposed to being a point-analysis tool, this non-invasive method captures the entire surface of interest. This means that its potential is not only in the discrimination of pigments but also in their mapping. However, the challenge lies in the fact that in a real painting, there is no clear-cut edge between regions with certain pure pigments or of the exact same mixture. Pigments and other paint materials mix seamlessly and continuously in the physical domain. In this article, we introduce a divergence-based approach to pigment discrimination and mapping. The methodology is then applied to Munch’s masterpiece The Scream (1893), whose pigments and materials have been identified for several points in the painting in a previous study. Through the introduced methodology, we have been able to extend the point analyzes of pigments and materials to the entire surface of the painting, recto and verso.

The development of innovative applications of imaging technologies for monitoring change in cultural heritage objects is central to the CHANGE-ITN project. Within this framework, the authors’ ongoing work targets the harnessing of hyperspectral imaging (HSI) for the documentation of cleaning treatments on the monumental University of Oslo Aula unvarnished oil paintings on canvas (1909–1916) by Edvard Munch (1863–1944). This is applicable to unvarnished paint surfaces in general. Particularly, this paper evaluates HSI techniques for the investigation of cleaning tests carried out in 2008, which targeted the removal of visible, ingrained particulate matter from Munch's unvarnished oil paints and exposed grounds on Kjemi (1914–1916). An exploratory in situ HSI campaign using visible to near-infrared (VNIR) and short-wave infrared (SWIR) cameras was undertaken to capture the soiled or previously cleaned areas on the painting's surface, with comparisons to analogous mock-ups. Principal component analysis (PCA) was used to extract information about possible trends with respect to the soiling. Results indicate that in both VNIR and SWIR ranges, the intensity of reflectance can be used to discriminate statistically between soiled and unsoiled/cleaned areas, whereas in SWIR, combination and first overtone bands of oil peaks can also serve as markers.

Recent developments in hyperspectral sensors allow us to obtain high spectral and spatial resolutions that are close to the optical and physical structures of acquired surfaces. Consequently, hyperspectral imaging is used for its potential gain of accuracy. To preserve this metrological potential, generated bias, errors, and uncertainties must be managed at all subsequent processing levels. Based on the argument that a spectral image processing should avoid the linear approach, this paper proposes several protocols for assessing the quality of spectral ordering relations. The protocols include considerations of theoretical properties to ensure result stability, in physical aspects of spectral processing to ensure the link to physical properties of materials, and experimental results using spectral images with physical ground truth to assess bias and errors. Full-band ordering relations are compared in order to find that which satisfies all of the expected properties of a metrological spectral image processing.

Texture perception plays a vital role in various fields, from computer vision to geology, influencing object recognition, image segmentation, and rock classification. Despite advances in convolutional neural networks (CNNs), their effectiveness in texture-based classification tasks, particularly in rock classification, still needs exploration. This paper addresses this gap by evaluating different CNN architectures using diverse publicly available texture datasets and custom datasets tailored for rock classification. We investigated the performance of 38 distinct models pre-trained on the ImageNet dataset, employing both transfer learning and fine-tuning techniques. The study highlights the efficacy of transfer learning in texture classification tasks and offers valuable perspectives on the performance of different networks on different datasets. We observe that while CNNs trained on datasets like ImageNet prioritize texture-based features, they face challenges in nuanced texture-to-texture classification tasks. Our findings underscore the need for further research to enhance CNNs’ capabilities in texture analysis, particularly in the context of rock classification. Through this exploration, we contribute insights into the suitability of CNN architectures for rock texture classification, fostering advancements in both computer vision and geology.

Hazy images suffer from low quality due to blurring, veiling effects, and low contrast. To improve their visibility, dehazing methods attempt to restore them to their corresponding clear scenes, often by focusing more on obtaining an accurate estimate based on a known ground truth. The perceptual quality of dehazed images, which can be described by means of objective and subjective quality assessments, is often not considered. This paper provides a quality assessment of dehazed images, focusing on aspects, e.g., color, image structure, and naturalness. Four image dehazing methods are considered, i.e., Contrast Limited Adapted Histogram Equalization (CLAHE), Dark Channel Prior and Refinement (DCP-R), Perception Inspired Deep Dehazing Network with Refinement (PDR-Net) and Conditional Generative Adversarial Network (CGAN) Pix2pix. The dehazing results are then put through objective and subjective assessments, for a comprehensive evaluation on image quality. Overall, Pix2pix shows the best results objectively, excelling in the recovery of color and image structure. Although it is outperformed by DCP-R in terms of naturalness, our subjective assessment shows that Pix2pix is also most preferred by human observers.