Content-Aware Despeckling for On-Demand Digital Image Processing
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Solution Overview
Problem
On-demand publishers face challenges in producing high-quality printed materials due to the presence of artifacts such as speckles and lines in digital images derived from scanned or photocopied sources, which detract from the overall quality of the printed product.
Innovation Solution
A networked environment with logical components for processing digital images, including deskew, segmentation, despeckling, border removal, and alignment, is used to prepare content for on-demand printing, employing iterative processes and layered despeckling to effectively remove artifacts and ensure consistent formatting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If digital images are used from scanned or photocopied sources, then content can be reproduced, but artifacts such as speckles and lines are introduced that detract from quality
Solution Approach 1:
The patent extracts and removes harmful artifacts (speckles and lines) from the digital image while preserving the legitimate content. The despeckling process identifies and extracts these harmful elements separately from the content, allowing the content to be reproduced without the degradation caused by artifacts.
Solution Approach 2:
The patent converts the harmful artifacts into a detectable pattern that can be systematically removed. By characterizing the artifacts as specific deviations from the expected image pattern, the system transforms them from unwanted noise into identifiable features that can be selectively eliminated through the despeckling algorithm.
2Productivity
If simple copying of source material is used, then processing time is minimized, but artifact removal is insufficient
Solution Approach 1:
The patent segments the image processing into distinct functional stages: content analysis, artifact identification, and selective removal. This segmentation allows each stage to be optimized independently, maintaining overall processing efficiency while achieving high precision in artifact removal through specialized algorithms applied to specific image regions.
Solution Approach 2:
The patent applies different processing qualities to different regions of the image based on their content characteristics. The despeckling algorithm adjusts its parameters and intensity dynamically across the image, applying stronger artifact removal to regions with higher artifact density while preserving fine details in text and illustration areas, thus achieving high precision without uniformly increasing processing time.
3Object-affected harmful factors
If aggressive artifact removal is applied, then quality improves, but content details may be degraded
Solution Approach 1:
The patent applies different artifact removal intensities to different image regions based on their semantic content. Text regions receive conservative processing to preserve legibility, while background and illustration regions receive more aggressive despeckling. The algorithm dynamically adjusts parameters based on local image characteristics, achieving high artifact removal effectiveness without degrading content fidelity.
Solution Approach 2:
The patent incorporates feedback mechanisms that continuously monitor the impact of artifact removal on image quality. The system analyzes the results of despeckling operations and adjusts subsequent processing parameters to prevent over-removal or degradation of legitimate content features, ensuring that artifact removal achieves the desired quality improvement without compromising content fidelity.
Data Source
AI summary
Systems and methods for removing artifacts from a page of digital image are presented. More particularly, a digital image is obtained, the digital image having at least one page of content to be processed. A content bounding box is determined for the content of the page. Additionally, a set of segments is generating, the set corresponding to particular areas of the content within the content bounding box, each area associated with a type of content. For each segment of the set of segments, the following are performed. Despeckling criteria are selected for identifying artifacts according to the type associated with the segment. Artifacts are identified in the segment according to the despeckling criteria. The identified artifacts are then removed from the page. Thereafter, the updated digital image is stored in a content store.