Color accuracy is of immense importance in various fields, including biomedical applications, cosmetics, and multimedia. Achieving precise color measurements using diverse lighting sources is a persistent challenge. Recent advancements have resulted in the integration of LED-based digital light processing (DLP) technology into many scanning devices for three-dimensional (3D) imaging, often serving as the primary lighting source. However, such setups are susceptible to color-accuracy issues. Our study delves into DLP-based 3D imaging, specifically focusing on the use of hybrid lighting to enhance color accuracy. We presented an empirical dataset containing skin tone patches captured under various lighting conditions, including combinations and variations in indoor ambient light. A comprehensive qualitative and quantitative analysis of color differences (ΔE00) across the dataset was performed. Our results support the integration of DLP technology with supplementary light sources to achieve optimal color correction outcomes, particularly in skin tone reproduction, which has significant implications for biomedical image analysis and other color-critical applications.
KEYWORDS: Fringe analysis, Digital Light Processing, Light sources and illumination, Skin, Projection systems, Light sources, Color tone, Biomedical applications, 3D modeling, Stereoscopy, Digital image processing
Color accuracy is crucial in several domains such as biomedical imaging, cosmetics, and multimedia. Digital Light Processing (DLP) with LEDs has increasingly become a popular lighting source in 3D scanning systems. Although DLP provides advantages in 3D reconstruction, it poses challenges in maintaining color accuracy. Our research focused on using hybrid lighting to improve the color accuracy of DLP-based 3D sensing systems. We developed an empirical dataset featuring skin tones captured under multiple lighting environments, including variations in indoor ambient lighting. Through qualitative and quantitative evaluations of color differences, we conclude that including auxiliary lighting with DLP is beneficial for color accuracy, particularly in biomedical imaging and other applications in which color accuracy is essential.
Fringe Projection Profilometry (FPP) with Digital Light Projector technology is one of the most reliable 3D sensing techniques for biomedical applications. However, besides the fringe pattern images,often a color texture image is needed for an accurate medical documentation. This image may be acquired either by projecting a white image or a black image and relying on ambient light. Color Constancy is essential for a faithful digital record, although the optical properties of biological tissue make color reproducibility challenging. Furthermore, color perception is highly dependent on the illuminant. Here, we describe a deep learning-based method for skin color correction in FPP. We trained a convolutional neural network using a skin tone color palette acquired under different illumination conditions to learn the mapping relationship between the input color image and its counterpart in the sRGB color space. Preliminary experimental results demonstrate the potential for this approach.
Retinal images are acquired with eye fundus cameras which, like any other camera, can suffer from dust particles attached to the sensor and lens. These particles impede light from reaching the sensor, and therefore they appear as dark spots in the image which can be mistaken as small lesions like microaneurysms. We propose a robust method for detecting dust artifacts from more than one image as input and, for the removal, we propose a sparse-based inpainting technique with dictionary learning. The detection is based on a closing operation to remove small dark features. We compute the difference with the original image to highlight the artifacts and perform a filtering approach with a filter bank of artifact models of different sizes. The candidate artifacts are identified via non-maxima suppression. Because the artifacts do not change position in the images, after processing all input images, the candidate artifacts which are not in the same approximate position in different images are rejected and kept unchanged in the image. The experimental results show that our method can successfully detect and remove artifacts, while ensuring the continuity of retinal structures, such as blood vessels.
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