Simulations of flatbed scanners can shorten the development cycle of new designs, estimate image quality, and lower manufacturing costs. In this paper, we present a flatbed scanner simulation a strobe RGB scanning method that investigates the effect of the sensor height on color artifacts. The image chain model from the remote sensing community was adapted and tailored to fit flatbed scanning applications. This model allows the user to study the relationship between various internal elements of the scanner and the final image quality. Modeled parameters include: sensor height, intensity and duration of illuminant, scanning rate, sensor aperture, detector modulation transfer function (MTF), and motion blur created by the movement of the sensor during the scanning process. These variables are also modeled mathematically by utilizing Fourier analysis, functions that model the physical components, convolutions, sampling theorems, and gamma corrections. Special targets were used to validate the simulation include single frequency pattern, a radial chirp-like pattern, or a high resolution scanned document. The simulation is demonstrated to model the scanning process effectively both on a theoretical and experimental level.
Multifunctional printers (MFP) are products that combine the functions of a printer, scanner, and copier. Our goal is to help customers to be able to easily diagnose scanner or print quality issues with their products by developing an automated diagnostic system embedded in the product. We specifically focus on the characterization of scanner motions, which may be defective due to irregular movements of the scan-head. The novel design of our test page and two-stage diagnostic algorithm are described in this paper. The most challenging issue is to evaluate the scanner performance properly when both printer and scanner units contribute to the motion errors. In the first stage called the uncorrected-print-error-stage, aperiodic and periodic motion behaviors are characterized in both the spatial and frequency domains. Since it is not clear how much of the error is contributed by each unit, the scanned input is statistically analyzed in the second stage called the corrected-print-error-stage. Finally, the described diagnostic algorithms output the estimated scan error and print error separately as RMS values of the displacement of the scan and print lines, respectively, from their nominal positions in the scanner or printer motion direction. We validate our test page design and approaches by ground truth obtained from a high-precision, chrome-on-glass reticle manufactured using semiconductor chip fabrication technologies.
Image classification is a prerequisite for copy quality enhancement in all-in-one (AIO) device that comprises a
printer and scanner, and which can be used to scan, copy and print. Different processing pipelines are provided
in an AIO printer. Each of the processing pipelines is designed specifically for one type of input image to achieve
the optimal output image quality. A typical approach to this problem is to apply Support Vector Machine to
classify the input image and feed it to its corresponding processing pipeline. The online training SVM can help
users to improve the performance of classification as input images accumulate. At the same time, we want to
make quick decision on the input image to speed up the classification which means sometimes the AIO device
does not need to scan the entire image to make a final decision. These two constraints, online SVM and quick
decision, raise questions regarding: 1) what features are suitable for classification; 2) how we should control the
decision boundary in online SVM training. This paper will discuss the compatibility of online SVM and quick
decision capability.
In the current market, reduction of warranty costs is an important avenue for improving profitability by manufacturers of printer products. Our goal is to develop an autonomous capability for diagnosis of printer and scanner caused defects with mid-range laser multifunction printers (MFPs), so as to reduce warranty costs. If the scanner unit of the MFP is not performing according to specification, this issue needs to be diagnosed. If there is a print quality issue, this can be diagnosed by printing a special test page that is resident in the firmware of the MFP unit, and then scanning it. However, the reliability of this process will be compromised if the scanner unit is defective. Thus, for both scanner and printer image quality issues, it is important to be able to properly evaluate the scanner performance. In this paper, we consider evaluation of the scanner performance by measuring its modulation transfer function (MTF). The MTF is a fundamental tool for assessing the performance of imaging systems. Several ways have been proposed to measure the MTF, all of which require a special target, for example a slanted-edge target. It is unacceptably expensive to ship every MFP with such a standard target, and to expect that the customer can keep track of it. To reduce this cost, in this paper, we develop new approach to this task. It is based on a self-printed slanted-edge target. Then, we propose algorithms to improve the results using a self-printed slanted-edge target. Finally, we present experimental results for MTF measurement using self-printed targets and compare them to the results obtained with standard targets.
Digital copiers are now widely used. One major issue for a digital copier is copy quality. In order to achieve as high quality as possible for every input document, multiple processing pipelines are included in a digital copier. Every processing pipeline is designed specifically for a certain class of document, which may be text, picture, or a mixture of both as is illustrated by the three examples shown in Fig. 1. In this paper, we describe an algorithm that can effectively classify an input image into its corresponding category. Publisher’s Note: The first printing of this volume was completed prior to the SPIE Digital Library publication and this paper has since been replaced with a corrected/revised version.
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