Search Results (21)

High-temperature thermal images enable monitoring and controlling processes in metal, semiconductors, and ceramic manufacturing but also monitor activities of volcanoes or contrasting wildfires. Infrared thermal cameras require knowledge of the emissivity coefficient, while multispectral pyrometers provide fast and accurate temperature measurements with limited spatial resolution. Bayer-pattern cameras offer a compromise by capturing multiple spectral bands with high spatial resolution. However, temperature estimation from color remains challenging due to spectral overlaps among the color filters in the Bayer pattern, and a widely accepted calibration method is still missing. In this paper, the quantum efficiency of an imaging system including the camera sensor, lens, and filters is inferred from a sequence of images acquired by looking at a black body source between 700 °C and 1100 °C. The physical model of the camera, based on the Planck law and the optimized quantum efficiency, allows the calculation of the Planckian locus in the color space of the camera. A regression neural network, trained on a synthetic dataset representing the Planckian locus, predicts temperature pixel by pixel in the 700 °C to 3500 °C range from live images. Experiments done with a color camera, a multispectral camera, and a furnace for heat treatment of metals as ground truth show that our calibration procedure leads to temperature prediction with accuracy and precision of a few tens of Celsius degrees in the calibration temperature range. Tests on a temperature-calibrated halogen bulb prove good generalization capability to a wider temperature range while being robust to noise. Full article

Given the challenges encountered by industrial cameras, such as the randomness of sensor components, scattering, and polarization caused by optical defects, environmental factors, and other variables, the resulting noise hinders image recognition and leads to errors in subsequent image processing. In this study, we propose a RAW image denoising method based on BlockwiseUNet. By enabling local feature extraction and fusion, this approach enhances the network’s capability to capture and suppress noise across multiple scales. We conducted extensive experiments on the SIDD benchmark (Smartphone Image Denoising Dataset), and the PSNR/SSIM value reached 51.25/0.992, which exceeds the current mainstream denoising methods. Additionally, our method demonstrates robustness to different noise levels and exhibits good generalization performance across various datasets. Furthermore, our proposed approach also exhibits certain advantages on the DND benchmark(Darmstadt Noise Dataset). Full article

Given the low accuracy of the traditional digital image correlation (DIC) method in complex deformation measurement, a color DIC method is proposed using a prism camera. Compared to the Bayer camera, the Prism camera can capture color images with three channels of real information. In this paper, a prism camera is used to collect color images. Relying on the rich information of three channels, the classic gray image matching algorithm is improved based on the color speckle image. Considering the change of light intensity of three channels before and after deformation, the matching algorithm merging subsets on three channels of a color image is deduced, including integer-pixel matching, sub-pixel matching, and initial value estimation of light intensity. The advantage of this method in measuring nonlinear deformation is verified by numerical simulation. Finally, it is applied to the cylinder compression experiment. This method can also be combined with stereo vision to measure complex shapes by projecting color speckle patterns. Full article

The two video satellites of the second and third batch of Zhuhai-1 microsatellites (referred to as OVS-2A/3A) are operational with their hyperspectral satellites, which improves the data acquisi-tion capability of the Zhuhai-1 remote sensing satellite constellation. Contrary to the linear array push-broom hyperspectral satellites and plane array CCD video satellites, the OVS satellite is equipped with a planar array Bayer pattern sensor, which can obtain single-band grayscale images by push-broom imaging. Additionally, the Bayer color reconstruction algorithm can interpolate sensor data to provide RGB color band information. Therefore, for the Bayer pattern push-broom sensor, the relative calibration method of linear push-broom or array cameras cannot be directly applied. The radiometric calibration of the Bayer pattern push-broom imaging mode has become a matter of concern; therefore, this study developed a radiometric calibration method for the Bayer pattern push-broom sensor of the OVS satellite and verified its effectiveness and accuracy. OVS images were used to perform on-orbit relative radiometric calibration, and the calibration accu-racy, including streaking metrics and root-mean-square error, was better than 1%, meeting the specification requirements for the OVS satellite. Visually, after calibration correction, the streaking and striping noise of the Bayer images was removed, and the radiometric quality of the image was considerably improved, providing a good data basis for subsequent research in remote sensing applications. Full article

Bayer color filter array (CFA) images are captured by a single-chip image sensor covered with a Bayer CFA pattern which has been widely used in modern digital cameras. In the past two decades, many compression methods have been proposed to compress Bayer CFA images. These compression methods can be roughly divided into the compression-first-based (CF-based) scheme and the demosaicing-first-based (DF-based) scheme. However, in the literature, no review article for the two compression schemes and their compression performance is reported. In this article, the related CF-based and DF-based compression works are reviewed first. Then, the testing Bayer CFA images created from the Kodak, IMAX, screen content images, videos, and classical image datasets are compressed on the Joint Photographic Experts Group-2000 (JPEG-2000) and the newly released Versatile Video Coding (VVC) platform VTM-16.2. In terms of the commonly used objective quality, perceptual quality metrics, the perceptual effect, and the quality–bitrate tradeoff metric, the compression performance comparison of the CF-based compression methods, in particular the reversible color transform-based compression methods and the DF-based compression methods, is reported and discussed. Full article

Digital cameras obtain color information of the scene using a chromatic filter, usually a Bayer filter, overlaid on a pixelated detector. However, the periodic arrangement of both the filter array and the detector array introduces frequency aliasing in sampling and color misregistration during demosaicking process which causes degradation of image quality. Inspired by the biological structure of the avian retinas, we developed a chromatic LED array which has a geometric arrangement of multi-hyperuniformity, which exhibits an irregularity on small-length scales but a quasi-uniformity on large scales, to suppress frequency aliasing and color misregistration in full color image retrieval. Experiments were performed with a single-pixel imaging system using the multi-hyperuniform chromatic LED array to provide structured illumination, and 208 fps frame rate was achieved at 32 × 32 pixel resolution. By comparing the experimental results with the images captured with a conventional digital camera, it has been demonstrated that the proposed imaging system forms images with less chromatic moiré patterns and color misregistration artifacts. The concept proposed verified here could provide insights for the design and the manufacturing of future bionic imaging sensors. Full article

This paper proposes a novel 3D discrete cosine transform (DCT) based image compression method for medical endoscopic applications. Due to the high correlation among color components of wireless capsule endoscopy (WCE) images, the original 2D Bayer data pattern is reconstructed into a new 3D data pattern, and 3D DCT is adopted to compress the 3D data for high compression ratio and high quality. For the low computational complexity of 3D-DCT, an optimized 4-point DCT butterfly structure without multiplication operation is proposed. Due to the unique characteristics of the 3D data pattern, the quantization and zigzag scan are ameliorated. To further improve the visual quality of decompressed images, a frequency-domain filter is proposed to eliminate the blocking artifacts adaptively. Experiments show that our method attains an average compression ratio (CR) of 22.94:1 with the peak signal to noise ratio (PSNR) of 40.73 dB, which outperforms state-of-the-art methods. Full article

As the color filter array (CFA)2.0, the RGBW CFA pattern, in which each CFA pixel contains only one R, G, B, or W color value, provides more luminance information than the Bayer CFA pattern. Demosaicking RGBW CFA images $I^{RGBW}$ is necessary in order to provide high-quality RGB full-color images as the target images for human perception. In this letter, we propose a three-stage demosaicking method for $I^{RGBW}$ . In the first-stage, a cross shape-based color difference approach is proposed in order to interpolate the missing W color pixels in the W color plane of $I^{RGBW}$ . In the second stage, an iterative error compensation-based demosaicking process is proposed to improve the quality of the demosaiced RGB full-color image. In the third stage, taking the input image $I^{RGBW}$ as the ground truth RGBW CFA image, an $I^{RGBW}$ -based refinement process is proposed to refine the quality of the demosaiced image obtained by the second stage. Based on the testing RGBW images that were collected from the Kodak and IMAX datasets, the comprehensive experimental results illustrated that the proposed three-stage demosaicking method achieves substantial quality and perceptual effect improvement relative to the previous method by Hamilton and Compton and the two state-of-the-art methods, Kwan et al.’s pansharpening-based method, and Kwan and Chou’s deep learning-based method. Full article

Low lighting images usually contain Poisson noise, which is pixel amplitude-dependent. More panchromatic or white pixels in a color filter array (CFA) are believed to help the demosaicing performance in dark environments. In this paper, we first introduce a CFA pattern known as CFA 3.0 that has 75% white pixels, 12.5% green pixels, and 6.25% of red and blue pixels. We then present algorithms to demosaic this CFA, and demonstrate its performance for normal and low lighting images. In addition, a comparative study was performed to evaluate the demosaicing performance of three CFAs, namely the Bayer pattern (CFA 1.0), the Kodak CFA 2.0, and the proposed CFA 3.0. Using a clean Kodak dataset with 12 images, we emulated low lighting conditions by introducing Poisson noise into the clean images. In our experiments, normal and low lighting images were used. For the low lighting conditions, images with signal-to-noise (SNR) of 10 dBs and 20 dBs were studied. We observed that the demosaicing performance in low lighting conditions was improved when there are more white pixels. Moreover, denoising can further enhance the demosaicing performance for all CFAs. The most important finding is that CFA 3.0 performs better than CFA 1.0, but is slightly inferior to CFA 2.0, in low lighting images. Full article

A joint demosaicing and denoising task refers to the task of simultaneously reconstructing and denoising a color image from a patterned image obtained by a monochrome image sensor with a color filter array. Recently, inspired by the success of deep learning in many image processing tasks, there has been research to apply convolutional neural networks (CNNs) to the task of joint demosaicing and denoising. However, such CNNs need many training data to be trained, and work well only for patterned images which have the same amount of noise they have been trained on. In this paper, we propose a variational deep image prior network for joint demosaicing and denoising which can be trained on a single patterned image and works for patterned images with different levels of noise. We also propose a new RGB color filter array (CFA) which works better with the proposed network than the conventional Bayer CFA. Mathematical justifications of why the variational deep image prior network suits the task of joint demosaicing and denoising are also given, and experimental results verify the performance of the proposed method. Full article

It is commonly believed that having more white pixels in a color filter array (CFA) will help the demosaicing performance for images collected in low lighting conditions. However, to the best of our knowledge, a systematic study to demonstrate the above statement does not exist. We present a comparative study to systematically and thoroughly evaluate the performance of demosaicing for low lighting images using two CFAs: the standard Bayer pattern (aka CFA 1.0) and the Kodak CFA 2.0 (RGBW pattern with 50% white pixels). Using the clean Kodak dataset containing 12 images, we first emulated low lighting images by injecting Poisson noise at two signal-to-noise (SNR) levels: 10 dBs and 20 dBs. We then created CFA 1.0 and CFA 2.0 images for the noisy images. After that, we applied more than 15 conventional and deep learning based demosaicing algorithms to demosaic the CFA patterns. Using both objectives with five performance metrics and subjective visualization, we observe that having more white pixels indeed helps the demosaicing performance in low lighting conditions. This thorough comparative study is our first contribution. With denoising, we observed that the demosaicing performance of both CFAs has been improved by several dBs. This can be considered as our second contribution. Moreover, we noticed that denoising before demosaicing is more effective than denoising after demosaicing. Answering the question of where denoising should be applied is our third contribution. We also noticed that denoising plays a slightly more important role in 10 dBs signal-to-noise ratio (SNR) as compared to 20 dBs SNR. Some discussions on the following phenomena are also included: (1) why CFA 2.0 performed better than CFA 1.0; (2) why denoising was more effective before demosaicing than after demosaicing; and (3) why denoising helped more at low SNRs than at high SNRs. Full article

Bauxites in southern France (Provence and Languedoc) have been exploited since the beginning of the last century. Though most of the deposits are now subeconomic or mined-out, these bauxites represent model analogs for other economic bauxites of the world. These Cretaceous karst-type deposits lie directly on Jurassic carbonates, and have been formed through a combination of different processes: in-situ alteration of siliciclastic sediments deposited on carbonate platforms, and reworking of early bauxites in the karst network. In this study, we present preliminary bulk rock geochemical and in-situ laser ablation (LA) -ICP-MS analyses on Al- and Fe-oxy-hydroxides of Provence (Les Baux-de-Provence) and Languedoc (Villeveyrac, Loupian) bauxites, with the aim of evaluating the concentrations of rare earth elements (REEs) and their deportment in these minerals. REEs have total average concentrations of 700 mg/kg in the analyzed samples, which are mostly composed of boehmite, γ-AlO(OH), and Fe-oxy-hydroxides (hematite and goethite). Maximum REEs concentrations are commonly associated with positive Ce anomalies in chondrite-normalized patterns. In contrast with other examples from the literature, it has been observed that high REE concentrations also occur in samples apparently devoid or poor of REE-minerals. In these samples, the total amount of REEs is positively correlated with that of Ga (commonly contained in boehmite). LA-ICP-MS trace element analyses on boehmite and Fe-oxy-hydroxides have shown that while the Al-hydroxide contains the suite of REEs, goethite and hematite are preferentially enriched only in Ce. Considering that Al-hydroxides are digested during the Bayer process, an interesting issue to develop in the future is whether (and how) REEs released during Al-hydroxide digestion could be recovered together with Al from the pregnant leach liquor, as routinely done for Ga. Full article

The RGBW color filter arrays (CFA), also known as CFA2.0, contains R, G, B, and white (W) pixels. It is a 4 × 4 pattern that has 8 white pixels, 4 green pixels, 2 red pixels, and 2 blue pixels. The pattern repeats itself over the whole image. In an earlier conference paper, we cast the demosaicing process for CFA2.0 as a pansharpening problem. That formulation is modular and allows us to insert different pansharpening algorithms for demosaicing. New algorithms in interpolation and demosaicing can also be used. In this paper, we propose a new enhancement of our earlier approach by integrating a deep learning-based algorithm into the framework. Extensive experiments using IMAX and Kodak images clearly demonstrated that the new approach improved the demosaicing performance even further. Full article

In this study, a method was suggested to produce dried powder from red mud (RM) sludge with 40%–60% water content without heating. The RM sludge is discharged from the Bayer process, which is used to produce alumina from bauxite ores. Nonthermally treated RM (NTRM) powder was produced by mixing RM sludge (50%), paper sludge ash (PSA, 35%), and high-calcium fly ash (HCFA, 15%). The physicochemical properties of NTRM were investigated by analyzing its water content, X-ray fluorescence spectra, X-ray diffraction patterns, and particle size. Moreover, to examine the applicability of NTRM as a construction material, slag cement mortar in which 20 wt% of the binder was replaced with NTRM was produced, and the compressive strength, porosity, and water absorption rate of the mortar were evaluated. Results indicated that NTRM of acceptable quality was produced when the water content in RM sludge decreased and CaO contained in PSA and HCFA reacted with moisture and formed portlandite. The NTRM-mixed mortar requires further examination in terms of durability because of the increased capillary voids and high water absorption rate, but its compressive strength is sufficient to enable its use in sidewalks, bike roads, and parking lots. Full article

This study analyzes the leaching behavior of elements from red mud (bauxite residue) at pH values ranging from 2 to 13. The leaching characteristics of metals and contaminated anions in five red mud samples produced by Bayer and combined processes were analyzed using the batch leaching technique following the US Environmental Protection Agency (USEPA) Method 1313. In addition, the geochemical model of MINTEQ 3.1 was used to identify the leaching mechanisms of metals. The results showed that Ca, Mg, and Ba follow the cationic leaching pattern. Al, As, and Cr show an amphoteric leaching pattern. The leaching of Cl⁻ is unaffected by the pH. The maximum leaching concentration of the proprietary elements occurs under extremely acidic conditions (pH = 2), except for As. The leaching concentration of F⁻ reaches 1.4–27.0 mg/L in natural pH conditions (i.e., no acid or base addition). At the same pH level, the leaching concentrations of Pb, As, Cr, and Cu are generally higher from red mud produced by the combined process than that those of red mud from the Bayer process. The leaching concentration of these elements is not strongly related to the total elemental concentration in the red mud. Geochemical modeling analysis indicates that the leaching of metal elements, including Al, Ca, Fe, Cr, Cu, Pb, Mg, Ba, and Mn, in red mud are controlled by solubility. The leaching of these elements depended on the dissolution/precipitation of their (hydr)oxides, carbonate, or sulfate solids. Full article