Dust in the scanner may cause vertical streaks in the scanned image since it reflects some part of the incident light. In this paper, we propose a method for detecting streaks in the scanned images that are a direct result of dust on the scanner glass. This lets customers resolve the issue without calling the maintenance. The solution includes denoising, conversion to opponent color space, calculation of ΔE', calculation of features, and classification. We denoise the image in order to remove halftones in case the image was halftoned. Opponent color space lets us look separately at luminance channel and chrominance channels. We have developed three features that use the data in luminance and chrominance channels. Eventually, we will use these features to detect streaks, and distinguish them from content.
The traditional diagnostics of print quality requires to print a professionally designed test-page and visually evaluated by an expert, which is very costly and time-consuming. Instead, a system that could automatically diagnose a customer's printer without any human's interference is proposed in this paper. The system relies on scanning user's printed output from user's printer. Print defects such as banding, streaking, etc. will be reflected on its scanned page and can be captured by comparing to its master image. The master image is the digitally generated original from which the page is printed. Once the print quality drops below a specified acceptance criteria level, the system can notify the user of the presence of print quality issues.. The current process has only concentrated on one type of print defect: text fading. The scanned page will initially be aligned with its master image with a feature based image registration algorithm. Text regions of the two pages are then extracted and compared directly.