Adaptive Multi-Frame Denoising via Detail Grade Map
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current multi-frame image denoising techniques face challenges in effectively distinguishing between detail regions and noisy or local motion regions, leading to imbalanced denoising and quality enhancement across the image.
Innovation Solution
A method that determines a detail grade map by calculating a difference map between a reference frame and a non-reference frame, local variance, and detail power map, which is then used to control adaptive spatial denoising power, applying stronger denoising in noisy regions and weaker denoising in detail regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If uniform denoising is applied across the entire image, then processing simplicity is maintained, but image quality deteriorates due to inability to distinguish detail regions from noisy regions
Solution Approach 1:
The patent applies local quality by computing a detail grade map that assigns different denoising strengths to different spatial regions. Regions with high detail content receive weaker denoising to preserve edges and textures, while regions with low detail content receive stronger denoising to remove noise. This spatially adaptive approach resolves the contradiction by tailoring processing intensity to local image characteristics rather than applying uniform treatment.
Solution Approach 2:
The patent segments the image into different regions based on detail content using the detail grade map. By dividing the image into high-detail and low-detail regions, the system can apply different denoising strategies to each segment, improving overall quality while managing complexity through structured regional processing.
2Object-affected harmful factors
If stronger denoising is applied to reduce noise, then noise reduction is improved, but detail preservation deteriorates due to over-smoothing
Solution Approach 1:
The patent resolves this contradiction by applying local quality through the detail grade map, which assigns different denoising strengths to different regions. In regions with high detail content (high detail grade), weaker denoising is applied to preserve edges and textures. In regions with low detail content (low detail grade), stronger denoising is applied to remove noise. This spatially adaptive strategy simultaneously achieves noise reduction and detail preservation.
3Manufacturing precision
If multi-frame processing is used to enhance image quality, then denoising performance is improved, but processing time increases due to additional computational steps
Solution Approach 1:
The patent applies preliminary action by pre-computing the detail grade map using difference maps and local variance calculations before the main denoising process. This preliminary characterization of image regions enables the subsequent adaptive denoising to proceed more efficiently by avoiding unnecessary computational steps in low-detail regions, thus reducing overall processing time while maintaining multi-frame denoising performance.
Data Source
AI summary
A method and system are provided. The method includes determining a difference map between a reference frame and a non-reference frame, determining a local variance of the reference frame, determining a detail power map based on a difference between the determined local variance and the determined difference map, and determining a detail grade map based on the determined detail power map.