Edge Pixel Enhancement for Text and Graphics in Digital Printers
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Solution Overview
Problem
Existing image quality enhancement techniques fail to effectively enhance text, graphic, or line art in documents, leading to degraded image quality that can hinder recognition and readability, especially due to blurring and loss of contrast caused by scanner resolution limitations.
Innovation Solution
A method and system that detect edge pixels in an input image, form a window around each pixel, calculate sums of active pixels, and determine the direction and type of transition to perform targeted enhancement, allowing for the enhancement of text, graphic, or line art in both horizontal and vertical directions based on black-to-white or white-to-black transitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If global image processing techniques are used for binarization and OCR, then overall image processing can be performed, but local image quality enhancement of text, graphic, or line art is not achieved
Solution Approach 1:
The patent divides the image processing into local regions by examining pixel neighborhoods and identifying edge pixels within specific windows. This segmentation allows different processing strategies to be applied to different parts of the image - specifically, edge enhancement is applied only to pixels identified as edges of text, graphics, or line art, while other regions receive different treatment. This resolves the contradiction by enabling localized quality enhancement without requiring global processing of the entire image.
Solution Approach 2:
The patent implements local quality enhancement by detecting edge pixels and applying enhancement operations specifically to those regions. The method calculates edge gradients and determines whether pixels represent text, graphic, or line art edges, then applies targeted enhancement to improve local image quality in those specific areas. This allows the system to enhance text, graphic, and line art quality locally while maintaining appropriate processing for other image regions.
2Productivity
If scanner resolution is reduced, then scanning speed and storage efficiency improve, but image quality degrades with indistinct edges and loss of contrast
Solution Approach 1:
The patent applies edge enhancement processing before the degraded image is used for OCR or printing. By detecting edge pixels and enhancing them in advance, the system compensates for the quality loss that would otherwise occur due to reduced scanner resolution. This preliminary action restores edge distinctness and contrast in the processed image, allowing lower-resolution scans to produce output quality comparable to higher-resolution scans.
Solution Approach 2:
The patent converts the harmful effect of resolution degradation into a benefit by using the same degraded image data to identify edge regions, then applying enhancement specifically to those edges. The method detects edges even in low-resolution images and enhances them, thereby transforming the initially degraded quality into improved local quality for text, graphics, and line art. This allows the system to achieve high-quality output from lower-resolution input.
3Measurement precision
If edge enhancement is applied to all pixels, then edge detection improves, but processing complexity and computation time increase
Solution Approach 1:
The patent segments the pixel population into edge pixels and non-edge pixels by examining each pixel's neighborhood and calculating edge gradients. Only pixels identified as edges of text, graphics, or line art undergo enhancement processing. This segmentation strategy significantly reduces the number of pixels that require complex enhancement operations, thereby lowering overall processing complexity while maintaining high edge detection accuracy for the relevant regions.
Solution Approach 2:
The patent applies partial action by enhancing only the necessary portions of the image - specifically, edge pixels that represent text, graphic, or line art boundaries. Rather than applying enhancement to all pixels uniformly, the method selectively enhances only those pixels that contribute to the quality of text, graphics, and line art. This partial approach reduces computational complexity while achieving the desired edge detection accuracy for the critical regions.
Data Source
AI summary
The present disclosure discloses methods and systems (a multi-function device or a digital printer) having capabilities for an object enhancement. In an example, the MFD includes a digital front end module to detect edge of the object and direction of the edge. Following the detection of the edge, the digital front end module classify the pixels of the edge into two categories, for example, which define that whether the edge pixels are created by black-to-white transition or white-to-black transition. Based on the classification of the edge pixels, the digital front end module performs the object enhancement either in black-to-white transition or in white-to-black transition.