Search Results (5)

Accurate skin color reproduction under varying CCT remains a critical challenge in the graphic arts, impacting applications such as face recognition, portrait photography, and human–computer interaction. Traditional AWB methods like gray-world or max-RGB often rely on statistical assumptions, which limit their accuracy under complex or extreme lighting. We propose SCR-AWB, a novel algorithm that leverages real skin reflectance data to estimate the scene illuminant’s SPD and CCT, enabling accurate skin tone reproduction. The method integrates prior knowledge of human skin reflectance, basis vectors, and camera sensitivity to perform pixel-wise spectral estimation. Experimental results on difficult skin color reproduction task demonstrate that SCR-AWB significantly outperforms traditional AWB algorithms. It achieves lower reproduction angle errors and more accurate CCT predictions, with deviations below 300 K in most cases. These findings validate SCR-AWB as an effective and computationally efficient solution for robust skin color correction. Full article

CubeSats are designed to optimize applications within the strict constraints of space and power. This paper presents an On-Board Image Enhancement technique for remote sensing payloads, focusing on achieving Auto White Balance (AWB) with limited resources and enhancing the capabilities of small/microsatellites. The study introduces hardware-based techniques, including histogram adjustment, De-Bayer processing, and AWB, all tailored to minimize hardware resource consumption on CubeSats. The integrated 1U CubeSat system comprises a sensor board, an Image Data Processor (IDP) unit, and onboard computing, with a total power consumption estimated at 2.2 W. This system facilitates image capture at a resolution of 1920 × 1200 and utilizes the proposed algorithm for image enhancement on remote sensing payloads to improve the quality of images captured in low-light environments, thereby demonstrating significant advancements in satellite image processing and object-detection capabilities. Full article

The successful investigation and prosecution of significant crimes, including child pornography, insurance fraud, movie piracy, traffic monitoring, and scientific fraud, hinge largely on the availability of solid evidence to establish the case beyond any reasonable doubt. When dealing with digital images/videos as evidence in such investigations, there is a critical need to conclusively prove the source camera/device of the questioned image. Extensive research has been conducted in the past decade to address this requirement, resulting in various methods categorized into brand, model, or individual image source camera identification techniques. This paper presents a survey of all those existing methods found in the literature. It thoroughly examines the efficacy of these existing techniques for identifying the source camera of images, utilizing both intrinsic hardware artifacts such as sensor pattern noise and lens optical distortion, and software artifacts like color filter array and auto white balancing. The investigation aims to discern the strengths and weaknesses of these techniques. The paper provides publicly available benchmark image datasets and assessment criteria used to measure the performance of those different methods, facilitating a comprehensive comparison of existing approaches. In conclusion, the paper outlines directions for future research in the field of source camera identification. Full article

Even though computer vision has been developing, edge detection is still one of the challenges in that field. It comes from the limitations of the complementary metal oxide semiconductor (CMOS) Image sensor used to collect the image data, and then image signal processor (ISP) is additionally required to understand the information received from each pixel and performs certain processing operations for edge detection. Even with/without ISP, as an output of hardware (camera, ISP), the original image is too raw to proceed edge detection image, because it can include extreme brightness and contrast, which is the key factor of image for edge detection. To reduce the onerousness, we propose a pre-processing method to obtain optimized brightness and contrast for improved edge detection. In the pre-processing, we extract meaningful features from image information and perform machine learning such as k-nearest neighbor (KNN), multilayer perceptron (MLP) and support vector machine (SVM) to obtain enhanced model by adjusting brightness and contrast. The comparison results of F1 score on edgy detection image of non-treated, pre-processed and pre-processed with machine learned are shown. The pre-processed with machine learned F1 result shows an average of 0.822, which is 2.7 times better results than the non-treated one. Eventually, the proposed pre-processing and machine learning method is proved as the essential method of pre-processing image from ISP in order to gain better edge detection image. In addition, if we go through the pre-processing method that we proposed, it is possible to more clearly and easily determine the object required when performing auto white balance (AWB) or auto exposure (AE) in the ISP. It helps to perform faster and more efficiently through the proactive ISP. Full article

We present and discuss parameters of a high dynamic range (HDR) image sensor with LED flicker mitigation (LFM) operating in automotive temperature range. The total SNR (SNR including dark fixed pattern noise), of the sensor is degraded by floating diffusion (FD) dark current (DC) and dark signal non-uniformity (DSNU). We present results of FD DC and DSNU reduction, to provide required SNR versus signal level at temperatures up to 120 °C. Additionally we discuss temperature dependencies of quantum efficiency (QE), sensitivity, color effects, and other pixel parameters for backside illuminated image sensors. Comparing +120 °C junction vs. room temperature, in visual range we measured a few relative percent increase, while in 940 nm band range we measured 1.46x increase in sensitivity. Measured change of sensitivity for visual bands—such as blue, green, and red colors—reflected some impact to captured image color accuracy that created slight image color tint at high temperature. The tint is, however, hard to detect visually and may be removed by auto white balancing and temperature adjusted color correction matrixes. Full article