Learned Piece-Wise Patch Regression for Image Detail Preservation
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Solution Overview
Problem
Existing image de-noising methods often cause undesirable blurring of features due to using a single input image for de-noising operations, which limits their effectiveness in preserving image details and accuracy.
Innovation Solution
The implementation of learned, piece-wise patch regression for image enhancement, where training patch pairs are used to determine patch-pair functions that transform noisy patches into clean patches, allowing for de-noising with minimal blurring and improved detail preservation by leveraging diverse variations in image content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a single input image is used for de-noising operations, then the de-noising process is simple and fast, but undesirable blurring of features occurs and image details are lost
Solution Approach 1:
The patent divides the image into multiple patches and processes them independently using patch-pair functions. Each patch is transformed separately based on learned relationships between noisy and clean patches, allowing detailed preservation while maintaining computational efficiency. This segmentation approach resolves the contradiction by enabling complex detail preservation without requiring overly complex global processing.
Solution Approach 2:
The patent pre-computes patch-pair functions from training data before processing new images. These learned functions map noisy patches to clean patches and are stored for rapid application. This preliminary action eliminates the need for complex real-time analysis during actual de-noising, allowing high detail preservation with simpler processing during execution.
2Measurement precision
If existing de-noising algorithms are used, then noise reduction is achieved, but blurring of image features and loss of sharpness occur
Solution Approach 1:
The patent uses learned patch-pair functions that copy relationships from training data where noisy patches are paired with their corresponding clean patches. These functions capture the mapping from noisy to clean representations, allowing accurate noise reduction while preserving sharpness by learning the optimal transformation from training examples rather than using generic filtering.
Solution Approach 2:
The patent changes the approach from fixed filtering parameters to learned patch-pair functions that adapt to different image content. The functions are determined from training data and can adjust their behavior based on the specific patch characteristics, enabling simultaneous noise reduction and sharpness preservation through data-driven parameter adaptation.
3Productivity
If single-image de-noising is performed, then processing time is short, but de-noising effectiveness and detail preservation are limited
Solution Approach 1:
The patent performs preliminary learning of patch-pair functions from training data offline, then applies these pre-learned functions rapidly to new images. This separates the computationally intensive learning phase from the fast application phase, maintaining high processing speed while improving de-noising effectiveness through the quality of learned transformations.
Solution Approach 2:
The learned patch-pair functions enable the system to process images autonomously using the knowledge gained from training data. The functions self-adapt to different image content based on the learned relationships, providing reliable de-noising without requiring iterative optimization or external intervention, thus maintaining fast processing speeds.
Data Source
AI summary
Systems and methods are provided for providing learned, piece-wise patch regression for image enhancement. In one embodiment, an image manipulation application generates training patch pairs that include training input patches and training output patches. Each training patch pair includes a respective training input patch from a training input image and a respective training output patch from a training output image. The training input image and the training output image include at least some of the same image content. The image manipulation application determines patch-pair functions from at least some of the training patch pairs. Each patch-pair function corresponds to a modification to a respective training input patch to generate a respective training output patch. The image manipulation application receives an input image generates an output image from the input image by applying at least some of the patch-pair functions based on at least some input patches of the input image.