Exploiting Superpixels for Multi-Focus Image Fusion

Ilyas, Areeba; Farid, Muhammad Shahid; Khan, Muhammad Hassan; Grzegorzek, Marcin

doi:10.3390/e23020247

Cited by 7 publications

(4 citation statements)

References 89 publications

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“…Usually, a score of 100 or less is viewed as indicating a match [30]. This formula is also known as color approximation distance (CAD) as it weighs the RGB values in order to better match human perception and, thus, obtain a more adequate color approximation [31]. The similarity score was derived from several tests on 64 colors.…”

Section: The Measurement Of the Color Differencementioning

confidence: 99%

Preliminary Identification of Mixtures of Pigments Using the paletteR Package in R—The Case of Six Paintings by Andreina Rosa (1924–2019) from the International Gallery of Modern Art Ca’ Pesaro, Venice

et al. 2023

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Frequently, the study of modern and contemporary paintings requires the taking of micro-samples to gain an in-depth understanding of the employed materials and techniques. However, since this procedure is characterized by its invasive nature, it must be carried out only if strictly necessary. This study aimed to evaluate the potentiality of K-means clustering to the corrected images of paintings to identify mixtures of pigments. This could assist in obtaining relevant preliminary information, facilitate the research process, and guide the sampling collection. Additionally, this method would be less expensive than the traditional multi-analytical approach as it would only require a modified digital camera, lenses, a color target and three computational resources for the processing of data (Imatest Master, Adobe Express—online, and R), out of which the latter two are freely available. The six paintings that have been selected for this study belong to the International Gallery of Modern Art Ca’ Pesaro in Venice (Italy) and have been depicted by Andreina Rosa (1924–2019), a Venetian artist. The artworks were thoroughly investigated mainly through non-invasive analytical techniques (FORS, RAMAN, ER-FTIR, EDXRF). Using cluster analysis, simulating mixtures, and calculating the color differences, it was possible to infer the existence of color mixtures of two/three detected primary colors from the examined images, which could be validated by the analytical results. Hence, it was concluded that samples taken from mixtures might suffice, since primary colors would be concomitantly analyzed.

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Section: The Measurement Of the Color Differencementioning

confidence: 99%

Preliminary Identification of Mixtures of Pigments Using the paletteR Package in R—The Case of Six Paintings by Andreina Rosa (1924–2019) from the International Gallery of Modern Art Ca’ Pesaro, Venice

et al. 2023

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“…Multi-focus image fusion can generate clear images of both distant and near scenes, and there are already some solutions available [67,70,74,[77][78][79]. In order to verify the generalization of the proposed method, extensive experiments were conducted on multifocus images in this section.…”

Section: Generalization Experiments On Multi-focus Image Fusionmentioning

confidence: 99%

DDFNet-A: Attention-Based Dual-Branch Feature Decomposition Fusion Network for Infrared and Visible Image Fusion

Wei,

Liu,

Jiang

et al. 2024

Remote Sensing

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The fusion of infrared and visible images aims to leverage the strengths of both modalities, thereby generating fused images with enhanced visible perception and discrimination capabilities. However, current image fusion methods frequently treat common features between modalities (modality-commonality) and unique features from each modality (modality-distinctiveness) equally during processing, neglecting their distinct characteristics. Therefore, we propose a DDFNet-A for infrared and visible image fusion. DDFNet-A addresses this limitation by decomposing infrared and visible input images into low-frequency features depicting modality-commonality and high-frequency features representing modality-distinctiveness. The extracted low and high features were then fused using distinct methods. In particular, we propose a hybrid attention block (HAB) to improve high-frequency feature extraction ability and a base feature fusion (BFF) module to enhance low-frequency feature fusion ability. Experiments were conducted on public infrared and visible image fusion datasets MSRS, TNO, and VIFB to validate the performance of the proposed network. DDFNet-A achieved competitive results on three datasets, with EN, MI, VIFF, QAB/F, FMI, and Qs metrics reaching the best performance on the TNO dataset, achieving 7.1217, 2.1620, 0.7739, 0.5426, 0.8129, and 0.9079, respectively. These values are 2.06%, 11.95%, 21.04%, 21.52%, 1.04%, and 0.09% higher than those of the second-best methods, respectively. The experimental results confirm that our DDFNet-A achieves better fusion performance than state-of-the-art (SOTA) methods.

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“…Generally a score of 100 or less could indicate a match [51]. Therefore, the RGB values are weighted in order to imitate human perception better and to get the best color approximation possible [52]. The limitations of this algorithm are its publication in an informal paper and the unavailability of the scientific data that led to its development [53,54].…”

Section: Simulation Of Mixtures Of Pigmentsmentioning

confidence: 99%

Towards a More Sustainable and Less Invasive Approach for the Investigation of Modern and Contemporary Paintings

et al. 2023

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In Heritage Science, sampling is frequently performed for the subsequent diagnostics of modern and contemporary paintings using invasive analytical techniques. However, it endangers the integrity of artworks, and thus, it should be carefully planned and carried out only as a last resort by specialists. Pigment mixtures have commonly been employed by modern and contemporary artists due to the ease of combining paints on the color palette. Hence, a painting might include both primary/secondary paints and mixtures of those. Therefore, obtaining a sample from a mixture might be sufficient for the identification of the individual primary-colored paints. This study focused on the creation of a user-friendly computational workflow for the analysis of images of paintings for the identification of mixtures using cluster analysis (K-means and Fuzzy C-means clustering). Sixteen modern and contemporary paintings that belong to the International Gallery of Modern Art Ca’ Pesaro in Venice have been selected: seven of them by Guido Cadorin (1892–1976), six by Andreina Rosa (1924–2019), and three by Boris Brollo (b. 1944), and the artworks of the latter being examined for the first time in this study (using Raman and ER–FTIR spectroscopies). It was found that mixtures can be identified in unvarnished paintings that consist of both non-overlapping and vibrant-colored paint layers, like those of Boris Brollo, and overlapping paint layers, like those of Andreina Rosa. Moreover, K-means clustering performs better in the case of non-overlapping colors, whereas Fuzzy C-means in the case of overlapping colors. In contrast, paintings that have been rendered with dark colors and that present a varnish layer, like those of Guido Cadorin, cannot be preliminary investigated in the proposed manner.

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Exploiting Superpixels for Multi-Focus Image Fusion

Cited by 7 publications

References 89 publications

Preliminary Identification of Mixtures of Pigments Using the paletteR Package in R—The Case of Six Paintings by Andreina Rosa (1924–2019) from the International Gallery of Modern Art Ca’ Pesaro, Venice

Preliminary Identification of Mixtures of Pigments Using the paletteR Package in R—The Case of Six Paintings by Andreina Rosa (1924–2019) from the International Gallery of Modern Art Ca’ Pesaro, Venice

DDFNet-A: Attention-Based Dual-Branch Feature Decomposition Fusion Network for Infrared and Visible Image Fusion

Towards a More Sustainable and Less Invasive Approach for the Investigation of Modern and Contemporary Paintings

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