The first International Electrotechnical Commission (IEC) standards for vision-based measurements of inkjet droplet speed and volume from inkjet print-heads were recently prepared for publication by the Technical Committee working group WG3 for Printed Electronics equipment. These IEC/ISO standards could be more widely adopted for other inkjet printing applications and establishing the relative performance of drop-on-demand (DoD) inkjet print-heads and inks. The underlying behavior of inkjet drops and effects influencing these standards for in-flight drop measurement methods are discussed.

This article presents a method of DCT-based illuminant compensation to enhance the accuracy of face recognition under an illuminant change. The basis of the proposed method is that the illuminant is generally located in low-frequency components in the DCT domain. Therefore, the effect of the illuminant can be compensated by controlling the low-frequency components. Moreover, a directional high-order local derivative pattern is used to detect robust features in the case of face motion. Experiments confirm the performance of the proposed algorithm, which achieved up to 96% when tested using a standard database.

Several objective methods have in recent years been proposed for the evaluation of print inhomogeneity. In spite of the similarity in some segments, these methods differ from each other in their basic principles, complexity and consideration of the human visual system. Most of the studies about their performance are based on a small number of observers, which from the statistical perspective reduces their credibility. In addition, there is inconsistency among researchers on the preference model that could be used in a standardized manner for an objective print inhomogeneity assessment. The aim of our study was to examine four commonly used objective methods: ISO 13660, Integration Model, Improved Integration Model and M-Score method. The methods were evaluated based on the correlation between their results and visual grades which were acquired with the graphical rating scale. Eight grayscale samples were created by digital simulation and printed with a high-quality inkjet printer. The samples were visually evaluated in a perception laboratory in accordance with the ISO 3664 and ASTM E1808-96(2009) standards. The results show that the Improved Integration Model and M-Score method outperform the other two methods, especially for the samples with systematic irregularities.

In this article, the authors introduce a new color image database, CHIC (Color Hazy Images for Comparison), devoted to haze model assessment and dehazing method evaluation. For three real scenes, they provide two illumination conditions and several densities of real fog. The main interest lies in the availability of several metadata parameters such as the distance from the camera to the objects in the scene, the image radiance and the fog density through fog transmittance. For each scene, the fog-free (ground-truth) image is also available, which allows an objective comparison of the resulting image enhancement and potential shortcomings of the model. Five different dehazing methods are benchmarked on three intermediate levels of fog using existing image quality assessment (IQA) metrics with reference to the provided fog-free image. This provides a basis for the evaluation of dehazing methods across fog densities as well as the effectiveness of existing dehazing dedicated IQA metrics. The results indicate that more attention should be given to dehazing methods and the evaluation of metrics to meet an optimal level of image quality. This database and its description are freely available at the web address http://chic.u-bourgogne.fr.

This work presents the use of graphical targets, namely Geometric Element Test Targets (GETT), to benchmark printer resolutions. The aim is to demonstrate the feasibility of GETT in distinguishing differences in the dimensional performance of printers of the same additive manufacturing (AM) process but manufactured by different makers. GETT are geometric shapes printed at different scale factors. The nature of GETT brings print failures to a visible level and can be analyzed with the assistance of graphical aids. Three Fused Deposition Modeling (FDM) systems from different manufacturers were employed to demonstrate the GETT’s ability in discerning the machines’ performance differences. The GETT targets can be analyzed quantitatively with topographic measurement tools or qualitatively with graphical aids. The latter technique allows the targets to be implemented in the AM process or workflow. GETT with graphical analysis offer a simple way to evaluate the manufacturing machine’s capability, create standards for consistency among machines and production runs, and provide references for customer interfaces.

In this article, a spatial gamut mapping algorithm (SGMA) which can preserve image details adaptively is proposed. First, the SGMA uses a guided filter to extract details that represent an image’s edges and textures from the luminance channel of the input image. The image details from outside the target gamut are then added back to the original image to address the detail loss that may be caused by gamut mapping. Then, based on the image details inside the target gamut obtained by statistics, the value of the “gamut mapping depth” is calculated dynamically for a specific value of the “in-gamut detail-preserving ratio.” Finally, in the detail-compensated image, the colors out of gamut are mapped into the target gamut uniformly according to the corresponding gamut mapping depth. The evaluation results based on paired-comparison experiments and image quality evaluation models show that the proposed SGMA has good performance on both preference and accuracy. According to the principle of the proposed SGMA, it has only one gamut mapping process for an original image, and there is no need to merge different frequency bands of the image. Therefore, the proposed SGMA not only improves the computational efficiency, but also fundamentally avoids the halo-artifacts caused by most SGMAs.

Images may be corrupted by noise during the process of acquisition and transmission. The wavelet thresholding method has been demonstrated to be a powerful approach for noise reduction. This paper presents a novel wavelet thresholding procedure to suppress the additive Gaussian noises in images. The method overcomes the discontinuity of using a hard thresholding function and reduces the constant bias of using a soft thresholding function. The experimental results show that the proposed denoising method outperforms standard wavelet denoising techniques, i.e., soft thresholding and hard thresholding, in addition to other existing improved methods, i.e., hyperbolic thresholding and exponential thresholding, in terms of the PSNR (peak signal to noise ratio), SNR (signal to noise ratio), MSE (mean-squared error) and Image Histogram, making it suitable for significantly improving image quality.