A measurement technique for the charge on a single toner particle using nanotweezers (microelectromechanical-systems-based actuated tweezers) and an atomic force microscopy cantilever has been developed. This unique system was designed to measure the image force on the cantilever induced by a charged toner particle gripped by nanotweezers. The toner charge was estimated from the image force by modeling the nonuniform charge distribution on the particle as an imaginary point charge. The comparison of the charges of single toner particles is possible because the calculated charge showed a linear correlation with that obtained using the blow-off method. This technique has great potential for application to the analysis of single isolated toner particles, such as those in fogging or transfer dust toner.

Gravure printing is one of the most promising high-speed, roll-to-roll techniques for printed electronics. The printing of electronic components is undergoing rapid changes from screen to gravure methods due to advances in imaging technology. In this study, by investigating specific printing parameters, the authors demonstrated that high-viscosity silver ink with a trench pattern is suitable for printing high-conductivity lines. To address the difficulties of transferring high-viscosity conductive ink, they developed a wiping gravure system in which the wiping device is used prior to the doctoring process. In addition, they evaluated the feasibility of applying the wiping device to gravure printing using a gravure printing testing machine, and certain characteristics of the printing process were analyzed through computer simulations. They sought to optimize the high-viscosity printing process by using the trench pattern, instead of common gravure cells. As a result, they were able to demonstrate printed lines with high resolution (50 μm width, 3.3 μm thickness) and low resistivity (7 ×10⁻⁵ Ω cm).

A framework for registration of multi-channel image data from a desktop line-scan multi-spectral camera system is presented. For this rather novel imaging technology, the source of image misalignment is distinct from other multi-spectral imaging approaches. The authors illustrate this by empirical analysis of channel misalignment from image data of a calibration target. This specifically designed target is generally useful for line-scan channel misalignment characterization and extraction of data for registration model fitting. It allows extraction of densely distributed key-points without the need of submillimetric precision in target manufacturing. For the camera system considered here, image channel misalignment was modeled by 1D polynomial functions of degree 4 and 1D multi-level uniform cubic B-splines. After image registration and image resampling by bilinear interpolation, subpixel accurate pixel alignment was achieved. For image data of a test scene, the initial maximum registration error of more than 4 pixels was reduced to about 0.3 pixel, which confirmed the high performance of both models experimentally. Apart from evaluating our proposed models, the authors also carried out comparisons with state-of-the-art registration models and demonstrated the advantages of our approach.

Flatbed document scanners enjoy widespread use for converting hardcopy documents into digital images. Improving image quality is a key issue for scanner manufacturers. This article proposes an Iterative Learning Control (ILC) based step-timing synthesis technique for reducing color registration error in stepper motor driven flatbed document scanners with no real-time feedback. To quantify color registration error, a line pattern image based technique is devised. Based on the measured color registration error signal, a single iteration convergent iterative learning control scheme is proposed to synthesize a stepper motor step-timing command for the scan region. Reduction in color registration error is achieved by compensating for velocity fluctuations in the constant speed portion of the motion. Experimental results demonstrate the effectiveness of the proposed approach in reducing color registration error in scanned image.

Magnetic resonance imaging (MRI) is one of most powerful medical imaging tools. However, the quality is affected by the noise pollution during the acquisition and transmission. A novel method is presented for adaptively learning the sparse dictionary while simultaneously reconstructing the image from noisy image data. The method is based on a K-singular value decomposition (K-SVD) algorithm for dictionary training on overlapping image patches of the noisy image. A modified dictionary update strategy with an effective control over the self-coherence of the trained dictionary is raised during the dictionary learning. The learned dictionary is employed to achieve effective sparse representation of the corrupted image and used to remove Rician noise, which shows a good performance in both noise suppression and feature preservation. The proposed method was compared with some current MRI denoising methods and the experimental results showed that the modified dictionary learning could obtain substantial benefits in denoising performance.

A traveling wave was used to ultrasonically drive the pressurized aqueous jet of a continuous inkjet multi-nozzle type printhead, resulting in remarkable uniformity in the break-off length due to non-resonant perturbation at each jet. Acoustic behaviors of the traveling wave were studied according to the jetting conditions. Propagation of the traveling wave was confirmed by decreasing the terminator length and observing unevenness in the break-off. Measurement of the shift in the phase angle at the drop break-off points allowed estimation of the velocity of the traveling wave, which is equivalent to the velocity of sound in water. Superposition of a harmonic wave onto the driving waveform verified that the measured sound velocity was appropriate by observing periodicities in satellite conditions and break-off length along with the sound propagation. A singular point in the velocity curve, believed to be due to the presence of suspended bubbles, appeared where the drop generation was irregular. The ink was degassed and the drop generation was observed to normalize; the drop generation became irregular again with the addition of non-degassed ink.

Degraded images with local color cast and low contrast can be improved by image quality enhancement techniques considering color, contrast, and other parameters related to a digital image. This article proposes a color image enhancement method which applies weighted multi-scale compensation coefficients to the gray world assumption algorithm based on a color constancy theory. A multi-scale Gaussian filter is used for computing the mean values of the local and global degraded colors, and calculating correction coefficients for size, pixel, and channel of the multi-scale filtered images independently based on the luminance of an image. Then, the weights are determined for a weighted sum of multi-scale correction coefficients by analyzing the local color distribution of the image. Next, the degraded colors are improved by utilizing the correction coefficients, which are integrated into the input image. Finally, the degraded color saturations are improved using the proposed weights. The experimental results show that, compared with conventional methods, the proposed method improves both the color and the contrast of various degraded images and produces better correction results.

Consumer time-of-flight (ToF) sensors are widely used for many imaging and vision applications. However, they have difficulty in measuring accurate depths of translucent objects due to the multiple paths of the infrared light emitted from the surface of a translucent object. The observed light at the sensor contains multiple bounces of emitted light from the surfaces of both the translucent foreground and the background, resulting in distorted depth measurement. In this article, the author adopts a practical two-layer surface model (translucent foreground overlaid on opaque background) and proposes a joint detection method for translucency and depth using a pair of depth observations obtained from a single ToF sensor at slightly different distances. Experimental results with real objects of various surface types show promising measurement results with this method.