Image Patch Partitioning for Noise Reduction Efficiency
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Solution Overview
Problem
Conventional image processing techniques for removing noise from images are resource-intensive and often result in blurring, limiting their functionality, especially in diverse capture settings like mobile devices with limited resources.
Innovation Solution
The implementation of patch partition and image processing techniques, where a system groups image patches into partitions, calculates image processing operators, and uses a database to apply these operators based on patch similarity, distinguishing between common and complex patches to efficiently process images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image processing techniques are used to remove noise, then image quality is improved, but computational resources are consumed excessively
Solution Approach 1:
The patent segments the image processing task by dividing patches into different categories (common patches and complex patches) based on their occurrence frequency and characteristics. Common patches are processed using pre-computed operators from an external database, while complex patches require internal processing. This segmentation enables resource-efficient processing by applying appropriate methods to different patch types.
Solution Approach 2:
The patent performs preliminary action by pre-computing image processing operators for common patches and storing them in an external database before actual image processing. This allows the system to retrieve pre-computed operators for common patches during image processing, avoiding the need to compute operators from scratch and significantly reducing computational resource consumption.
2Manufacturing precision
If conventional image processing techniques are used to remove noise, then image quality is improved, but processing time increases
Solution Approach 1:
The patent segments patches into common and complex categories, allowing the system to process common patches quickly by retrieving pre-computed operators from the external database rather than performing time-consuming internal processing. This segmentation strategy significantly reduces overall processing time while maintaining image quality.
Solution Approach 2:
By pre-computing and storing image processing operators for common patches in the external database, the system eliminates the need to perform time-consuming computations during actual image processing. The retrieval of pre-computed operators is much faster than computing operators from scratch, thereby reducing processing time.
3Productivity
If patch partitioning is implemented, then processing efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements segmentation by dividing patches into partitions based on their characteristics (common vs. complex). This segmentation, combined with the external database approach, improves processing efficiency by allowing quick retrieval of appropriate operators. The system manages complexity through a clear partitioning strategy and database architecture.
Solution Approach 2:
The external database serves as an intermediary between the image processing system and the pre-computed operators. This intermediary component simplifies the system architecture by centralizing stored operators and enabling efficient retrieval, thereby improving processing efficiency while managing system complexity through a well-defined intermediary layer.
Data Source
AI summary
Patch partition and image processing techniques are described. In one or more implementations, a system includes one or more modules implemented at least partially in hardware. The one or more modules are configured to perform operations including grouping a plurality of patches taken from a plurality of training samples of images into respective ones of a plurality of partitions, calculating an image processing operator for each of the partitions, determining distances between the plurality of partitions that describe image similarity of patches of the plurality of partitions, one to another, and configuring a database to provide the determined distance and the image processing operator to process an image in response to identification of a respective partition that corresponds to a patch taken from the image.