Tree-Based Linear Regression for Image Denoising
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Solution Overview
Problem
Conventional image denoising techniques often result in blurring and consume significant resources due to training size issues, failing to effectively remove noise from images captured in varied settings with limited light or device capabilities.
Innovation Solution
A patch-based approach that combines self-similar patches within an image with a database of clean patches to learn a denoising operator, applying it to noisy patches to reduce and eliminate noise, leveraging a tree-based linear regression framework for efficient denoising.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image denoising techniques are used, then noise removal is attempted, but the images become blurred and resource consumption increases
Solution Approach 1:
The image is divided into multiple patches, and each patch is processed independently through tree-based linear regression. This segmentation allows the algorithm to handle local variations in noise patterns while preserving edge details and avoiding the blurring effect of global denoising methods.
Solution Approach 2:
The patent applies different denoising operators to different partitions of the image based on local characteristics. By training multiple linear regression models on different patches and selecting the most appropriate operator for each region, the method preserves local image quality and details while effectively removing noise.
2Reliability
If conventional image denoising techniques are used, then noise removal is attempted, but resource consumption increases significantly
Solution Approach 1:
The image processing is segmented into independent patch operations, each using a pre-trained linear regression model. This approach reduces computational complexity compared to conventional methods that process entire images or large overlapping regions, thereby reducing resource consumption while maintaining denoising effectiveness.
Solution Approach 2:
The patent changes the fundamental parameter of the denoising approach by using linear regression parameters instead of conventional filtering parameters. This mathematical transformation allows for more efficient computation with reduced resource requirements while achieving comparable or superior noise removal performance.
3Measurement precision
If fine-grained partitions are used for denoising, then denoising precision is improved, but device complexity increases
Solution Approach 1:
The image is partitioned into fine-grained patches that are processed independently. This segmentation enables precise local denoising while keeping each individual processing unit simple through the use of linear regression, thereby achieving high precision without proportionally increasing overall system complexity.
Solution Approach 2:
Pre-trained linear regression models are created for different partitions and then applied repeatedly to similar patches throughout the image. This copying approach allows fine-grained processing precision to be achieved without the complexity of training models for every single patch, as the same models can be reused across multiple regions.
Data Source
AI summary
Image denoising techniques are described. In one or more implementations, a denoising result is computed by a computing device for a patch of an image. One or more partitions are located by the computing device that correspond to the denoising result and a denoising operator is obtained by the computing device that corresponds to the located one or more partitions. The obtained denoising operator is applied by the computing device to the image.