Self-adaptive Image Edge Correction Device for Blurring and Saw-tooth Reduction
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Solution Overview
Problem
Existing image enlargement techniques, such as polynomial interpolation, result in image blurring and saw-tooth phenomena due to filtering out high-frequency components, leading to unsatisfactory image interpolation performance and high computational costs or time consumption in correction methods.
Innovation Solution
A self-adaptive image edge correction method involving a sharpening filter, superimposer, and edge detector to enhance images, determine edge deviations, and apply selective horizontal or vertical corrections to address blurring and saw-tooth issues, utilizing a Laplacian filter and specific correction equations to produce a smooth, corrected image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If polynomial interpolation is used to enlarge the image, then the image resolution is improved, but edge blurring and saw-tooth phenomena occur
Solution Approach 1:
The image processing is segmented into distinct functional modules: polynomial interpolation unit for magnification, edge detection unit for identifying edge locations, and edge correction unit for correcting saw-tooth phenomena. This segmentation allows each module to specialize in one aspect of image processing, resolving the contradiction by handling resolution enhancement and edge quality preservation as separate but coordinated tasks.
Solution Approach 2:
The edge correction unit applies corrective measures specifically to detected edge regions rather than processing the entire image uniformly. By detecting edge locations and applying correction only where needed, the system maintains high resolution benefits while preserving edge quality locally, thus resolving the contradiction between overall image resolution and specific edge quality.
2Manufacturing precision
If Low-Pass Filter is used to improve edge saw-tooth, then the saw-tooth phenomenon is reduced, but the entire image becomes distorted
Solution Approach 1:
Instead of applying a global Low-Pass Filter that distorts the entire image, the invention applies edge correction only to detected edge regions. The edge correction unit uses the detected edge locations to selectively adjust only the problematic saw-tooth areas, preserving the fidelity of the rest of the image while still improving edge smoothness.
Solution Approach 2:
The edge detection unit acts as an intermediary that identifies edge locations, which then guides the edge correction unit to apply corrections only where necessary. This intermediary step prevents blanket application of correction filters that would distort the entire image, allowing selective correction that maintains overall image fidelity.
3Manufacturing precision
If Error-Amended Sharp Edge or Linear Minimum Mean Square-Error Estimation is used to improve edges, then edge quality is improved, but computation amount and time increase significantly
Solution Approach 1:
The invention applies partial correction by focusing computational resources only on detected edge regions rather than processing the entire image. The edge detection unit identifies specific locations requiring correction, and the edge correction unit applies corrections only to those regions, significantly reducing computation amount and time while maintaining edge accuracy.
Solution Approach 2:
The processing is segmented into edge detection and edge correction phases, with each phase handling specific tasks. This segmentation avoids the excessive computation of comprehensive methods by dividing the problem into manageable parts, improving computational efficiency while maintaining edge quality.
4Manufacturing precision
If Fast Edge-Oriented Interpolation or Grey Polynomial Interpolation is used, then edge processing is improved, but repeated threshold tests make the process time-consuming
Solution Approach 1:
The edge detection unit performs preliminary identification of edge locations before the interpolation and correction processes. By detecting edges in advance, the system avoids repeated threshold tests during interpolation, as the edge locations are already known. This preliminary action significantly reduces computation time while maintaining interpolation quality.
Solution Approach 2:
The edge detection results provide feedback to guide the interpolation and correction processes. The detected edge locations inform where polynomial interpolation should be applied and where correction is needed, eliminating the need for repeated threshold tests and reducing overall computation time while maintaining accuracy.
Data Source
AI summary
A self-adaptive image edge correction device and method thereof, including an image fetching unit, an image processing unit, and an image output unit. Wherein, the image fetching unit is used to provide an original image, and is connected electrically to the image processing unit, that includes a sharpening filter, a superimposer, and an edge detector. The sharpening filter converts the original image into a sharpened edge image, and the superimposer superimposes the original image to the sharpened edge image, to form an enhanced image. The edge detector fetches the edge of the enhanced image, to obtain a differential edge image. The image processing unit then utilizes selectively the horizontal correction or vertical correction to correct the differential edge image, based on s deviation direction of the differential edge image, to form a corrected image, and provide it to the image output unit to output as required.