Object-Region Adaptive Noise Removal for Image Processing
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Solution Overview
Problem
Existing image processing systems struggle to effectively remove noise from image data without erroneously removing objects like characters or figures, especially when the noise is small in size, leading to suboptimal character recognition accuracy.
Innovation Solution
An image processing apparatus that acquires image data, specifies object regions, sets a size threshold for noise removal based on the object region's size, and removes noise smaller than the threshold, adjusting the threshold according to the object's size and attributes to prevent erroneous removal of characters or figures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a fixed noise removal threshold is applied to all object regions, then noise removal processing is simplified, but small objects like characters or figures may be erroneously removed along with noise
Solution Approach 1:
The noise removal threshold is made dynamic by setting it according to the size of each object region. The specifying unit identifies object regions containing characters or figures, and the setting unit adjusts the threshold based on the area of these regions. This allows the threshold to adapt to different object sizes, preventing erroneous removal of small objects while maintaining noise removal effectiveness.
Solution Approach 2:
Different noise removal thresholds are applied to different object regions based on their local characteristics. The setting unit determines appropriate thresholds for each specified object region, allowing small regions (containing characters or figures) to have lower thresholds while larger regions can tolerate higher thresholds. This local adaptation prevents character removal errors in critical areas.
2Reliability
If a lower noise removal threshold is used to prevent character removal errors, then character recognition accuracy is maintained, but larger noise regions cannot be effectively removed
Solution Approach 1:
The threshold dynamically adjusts based on object region size. For small object regions (likely containing characters or figures), a lower threshold preserves these elements. For large object regions (less likely to contain important elements), a higher threshold enables more aggressive noise removal. This dynamic adjustment resolves the contradiction between preserving small objects and removing large noise regions.
Solution Approach 2:
The noise removal process applies different thresholds to different spatial locations based on object region characteristics. Small regions receive protective low thresholds, while large regions receive aggressive high thresholds. This localized quality adjustment ensures character recognition accuracy is maintained where needed while maximizing noise removal where safe.
3Productivity
If noise removal is performed without considering object region size, then processing speed is maintained, but precision of noise removal deteriorates
Solution Approach 1:
The image processing is segmented into distinct stages: first, object regions are specified using existing OCR technology to identify areas containing characters or figures; second, thresholds are set for each segmented region based on its size; third, noise removal is performed with region-specific thresholds. This segmentation allows precise noise removal without significantly increasing overall processing time.
Solution Approach 2:
Object region specification is performed as a preliminary action before noise removal. By pre-identifying regions containing characters or figures and calculating appropriate thresholds in advance, the system prepares region-specific parameters that enable precise noise removal during the main processing stage, balancing speed and precision.
Data Source
AI summary
An image processing apparatus comprises: an acquiring unit configured to acquire image data generated by reading an original; a specifying unit configured to specify an object region including a predetermined object in the image data; a removing unit configured to remove, from the object region, noise with a size smaller than a size specified by a first threshold; and a setting unit configured to set the first threshold, for each object region specified by the specifying unit, in accordance with the size of the object region.