Temporal Filtering with Local Neighborhood Constraints
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Solution Overview
Problem
Conventional temporal filters often produce visible motion artifacts in moving areas due to strong filtering, which reduces noise reduction effectiveness.
Innovation Solution
A method for temporal filtering with local neighborhood limitations that adjusts sample values within a bounded range based on minimum and maximum values of local samples, allowing for different neighborhood sizes and excursion limits in multiple iterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If strong motion compensated temporal filtering is applied to moving areas, then noise filtering effectiveness is improved, but visible motion artifacts are generated
Solution Approach 1:
The patent applies different filtering strengths to different spatial locations by introducing a local neighborhood limitation mechanism. For each pixel, the filtering is constrained by the minimum and maximum values within its local neighborhood, ensuring that filtered values remain consistent with surrounding areas. This local quality approach prevents strong filtering in moving areas while maintaining effective noise reduction in still areas, thereby resolving the contradiction between noise filtering effectiveness and motion artifact generation.
2Manufacturing precision
If temporal filtering strength is increased to filter still areas more effectively, then noise reduction is improved, but moving areas suffer from strong filtering artifacts
Solution Approach 1:
The invention implements spatially adaptive filtering by constraining each pixel's filtered value to lie within the range defined by its local neighborhood. This local quality mechanism allows strong filtering in still areas where neighborhood values are consistent, while automatically reducing filtering strength in moving areas where neighborhood values vary significantly, thus improving noise reduction quality without generating artifacts.
Solution Approach 2:
The patent introduces dynamic adjustment of filtering strength based on local image characteristics. The filtering process dynamically determines the appropriate filtering level for each pixel by examining its local neighborhood, making the filtering adaptive rather than uniform. This dynamic approach enables the system to achieve high noise reduction in still areas while preserving motion details, resolving the contradiction between filtering precision and artifact generation.
Data Source
AI summary
A method for temporal filtering with local neighborhood limitations is disclosed. Step (A) of the method may perform a temporal filter operation on a target sample in a current picture of a sequence of pictures to generate an intermediate value of the target sample. Step (B) may find a minimum value and a maximum value among a plurality of local samples in a local neighborhood in the current picture around the target sample. The minimum value and the maximum value generally bound a minimum/maximum range. Step (C) may adjust the intermediate value toward the minimum/maximum range to generate a final value of the target sample if the intermediate value is outside a limited range. The limited range is generally based on the minimum/maximum range.