GPU Feature Erasure Along Radial Pixel Paths
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Solution Overview
Problem
Existing image processing technologies struggle to efficiently erase high-contrast features like text and signatures from documents on darker backgrounds without introducing latency due to CPU-GPU memory copying, which is resource-intensive.
Innovation Solution
Utilizing a graphics processing unit (GPU) to set the color value of each pixel along a line outward from the image center to the edge, effectively erasing features by setting them to the lightest color value of sampled pixels, thereby simplifying edge detection and reducing computational burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If CPU-based image processing is used to erase high-contrast features, then processing can be performed, but latency increases and resource utilization decreases due to CPU-GPU memory copying
Solution Approach 1:
The patent uses the GPU as an intermediary processing unit that performs feature erasure directly on the image data without requiring data transfer between CPU and GPU memory. The GPU shader program acts as the intermediary that implements the erasure algorithm, eliminating the memory copying bottleneck and reducing latency while maintaining high processing throughput.
2Productivity
If traditional image processing methods are used to detect and remove features, then features can be erased, but computational burden increases and resource utilization decreases
Solution Approach 1:
The patent replaces complex mechanical image processing operations with a simplified GPU-based approach. Instead of using traditional CPU-based algorithms for feature detection and removal, the system uses a GPU shader that processes pixels in parallel, substituting the mechanical complexity of sequential processing with the parallel architecture of the GPU, thereby reducing computational burden while improving efficiency.
3Difficulty of detecting and measuring
If high-contrast features are present in the image, then feature detection becomes easier, but feature erasure becomes more difficult without introducing artifacts
Solution Approach 1:
The patent applies local quality by processing each pixel independently based on its local neighborhood characteristics. The shader program examines the contrast between each pixel and its neighbors to determine whether it belongs to a high-contrast feature, allowing the erasure operation to adapt locally to the image content. This approach simplifies the erasure process by treating each location uniquely based on its local properties rather than requiring global analysis.
Data Source
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AI summary
A GPU receives an image comprising an array of pixels. The image depicts features in a field of an object on a background. The features and the background contrasting with the object field, and at least a portion of the object is at the center of the image. In parallel for each particular pixel of a first plurality of the pixels, the GPU sets the color value of the particular pixel to the lightest color value of a second plurality of the pixels substantially along a line outward from the particular pixel toward an edge of the image. The line can be defined by the particular pixel and the image center.