Image Processor Tonal Transformation for Text Sharpness
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Solution Overview
Problem
Gamma correction in image processing reduces contrast, affecting the quality of small and fine text or images, and existing methods do not effectively differentiate between areas requiring intensity corrections while preserving sharpness.
Innovation Solution
An image processor extracts image data portions, assigns labels to each portion, and applies distinct tonal transformations based on these labels to address the issue of contrast reduction, using techniques like dynamic gamma correction, adaptive thresholding, and binary morphology to identify and process text and image areas differently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If gamma correction is applied to image data, then intensity values are transformed, but contrast is reduced affecting quality of small and fine text
Solution Approach 1:
The patent segments the image into multiple portions (text areas, image areas, background areas) and applies different tonal transformations to each segment. This allows gamma correction to be applied selectively to background areas while preserving contrast in text areas, thereby resolving the contradiction between intensity transformation and contrast quality.
Solution Approach 2:
The patent applies different quality characteristics to different regions of the image. Specifically, text areas are processed with transformations that preserve sharpness and contrast, while background areas receive full gamma correction. This local differentiation resolves the contradiction by allowing intensity transformation where needed while maintaining contrast quality where critical.
2Ease of manufacture
If uniform tonal transformation is applied to entire image, then processing is simple, but sharpness of text areas is degraded
Solution Approach 1:
The patent divides the image into distinct portions (text, image, background) and applies different tonal transformations to each. This segmentation approach maintains processing simplicity through automated classification while preserving text sharpness by excluding text areas from aggressive gamma correction, thus resolving the contradiction between processing simplicity and text sharpness.
Solution Approach 2:
The patent performs preliminary classification of image portions before applying tonal transformations. By identifying and labeling text areas, image areas, and background areas in advance, the system prepares the image for selective processing. This preliminary action enables simple automated processing while ensuring text sharpness is preserved through appropriate transformation selection.
Data Source
AI summary
A method of performing tonal transform on image data. The method includes extracting a portion of the image data, assigning one of a plurality of labels to the extracted portion, performing a first tonal transformation on the extracted portion if a first label is assigned to the extracted portion, and performing a second tonal transformation on the extracted portion if a second label is assigned to the extracted portion. The method can be performed, for example, with an image processor comprising a memory that stores the image data and a processor coupled to the memory.