Image Blurring Apparatus Using Bitwise Operations on Sample Images
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Solution Overview
Problem
Conventional image blurring algorithms are inefficient for large-resolution images, requiring high-performance hardware and increasing costs, and can reduce graphic user interface reactivity and cause output frame drops, especially when applied to autonomous vehicles and robotics.
Innovation Solution
An apparatus and method that extracts a sample image from the original image, adjusts its scale, and performs a bitwise operation applying the same blurring value to adjacent pixel regions, reducing noise removal time by calculating a blur generation area and applying the blurring value to pixels in the sample image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image blurring algorithm is applied to large-resolution images, then noise removal function is achieved, but processing time increases and hardware cost increases
Solution Approach 1:
The patent divides the large-resolution image into multiple small regions (e.g., 16x16 pixel blocks) and processes each region independently. This segmentation allows the system to apply the same blurring algorithm to many small areas rather than processing the entire large image at once, significantly reducing processing time while maintaining noise removal effectiveness in each region.
Solution Approach 2:
The patent applies blurring processing to only specific regions of the image that require noise removal, rather than processing the entire image uniformly. By identifying and processing only the necessary portions (partial action), or applying excessive processing to certain areas that can be later refined, the system optimizes processing time while achieving the required noise removal quality.
2Reliability
If conventional image blurring algorithm is applied to large-resolution images, then noise removal function is achieved, but hardware configuration cost increases
Solution Approach 1:
The patent segments the large image into smaller processing units that can be handled by standard hardware configurations. By dividing the image into manageable blocks, the system can use conventional processors and memory resources effectively, avoiding the need for expensive high-performance hardware while still achieving noise removal across the entire large-resolution image.
Solution Approach 2:
The patent creates multiple copies of the same blurring processing logic or template and applies them across different regions of the image simultaneously. This copying approach allows standard hardware to process multiple image regions in parallel or sequential batches, eliminating the need for specialized high-performance hardware while maintaining effective noise removal across the complete image.
3Illumination intensity
If image blurring is applied to graphics, then visual effect is achieved, but GUI reactivity decreases
Solution Approach 1:
The patent divides the graphical interface into multiple small regions and applies blurring effects to only those regions that require visual emphasis or noise reduction, rather than applying the effect to the entire GUI. This segmentation maintains the visual impact where needed while preserving the responsiveness and reactivity of the overall user interface.
Solution Approach 2:
The patent applies blurring effects partially to specific graphical elements or regions that benefit from the visual effect, rather than applying it uniformly across the entire interface. This selective application maintains GUI reactivity and responsiveness while achieving the desired visual impact in targeted areas, preventing overall system slowdown.
Data Source
AI summary
Provided is an apparatus for processing image blurring configured to extract a sample image by calculating a blur generation area through scales of a horizontal length and a vertical length of an original image, read a pixel value corresponding to the blur generation area calculated from the original image and perform a bitwise operation on the sample image, and set, at a pixel of the sample image changed by the bitwise operation, a result value of the bitwise operation. In this case, the bitwise operation performed on the sample image includes applying the same blurring value on n pixel regions adjacent to a corresponding pixel of the sample image to reduce the time for removing image noise.