Block-Based Noise Reduction Intensity for Image Frames
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Solution Overview
Problem
Conventional image processing methods struggle to effectively reduce noise without blurring the frame, as strong noise reduction can eliminate object information, while weak noise reduction fails to adequately remove noise.
Innovation Solution
An image processing method and device that divide frames into blocks based on a block-size value, calculate a noise reduction intensity array from pixel information of each block, and generate an output frame using this array to improve noise reduction capability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If strong noise reduction is applied to the captured frame, then noise can be effectively reduced, but object information is removed together with noise resulting in a blurry frame
Solution Approach 1:
The patent divides the captured frame into multiple blocks and further segments each block into sub-blocks for processing. This segmentation allows different noise reduction intensities to be applied to different regions, reducing noise effectively while preserving object information in motion areas.
Solution Approach 2:
The patent calculates motion information for each block and determines noise reduction parameters locally based on motion characteristics. Blocks with high motion have lower noise reduction intensity to preserve object details, while blocks with low motion have higher noise reduction intensity to remove noise effectively.
2Loss of information
If weak noise reduction is applied to the captured frame, then object information is preserved, but noise cannot be effectively reduced
Solution Approach 1:
By segmenting the frame into blocks and sub-blocks, the patent can apply weak noise reduction only where necessary (in motion areas) while applying stronger reduction in static areas, thus preserving object information overall while still reducing noise effectively.
Solution Approach 2:
The patent applies different noise reduction strengths locally based on motion analysis. Static regions receive stronger noise reduction while motion regions receive weaker reduction, achieving both noise removal and object preservation simultaneously across different parts of the image.
3Measurement precision
If pixel-by-pixel noise reduction is used, then processing precision is maintained, but processing complexity increases and noise reduction capability is limited
Solution Approach 1:
The patent processes images in blocks and sub-blocks rather than pixel-by-pixel, reducing computational complexity while maintaining precision through local motion analysis and adaptive parameter calculation for each block.
Solution Approach 2:
The patent combines multiple processing steps including motion estimation, block segmentation, and adaptive noise reduction into an integrated framework that operates on blocks rather than individual pixels, simplifying the overall processing complexity while maintaining effectiveness.
Data Source
AI summary
An image processing method includes the following steps. Firstly, a block-size value is obtained. Then, a first frame into a plurality of first blocks according to the block-size value is divided. Then, a second frame into a plurality of second blocks according to the block-size value is divided. Then, a noise reduction intensity array is obtained according to a first pixel information of each first block and a second pixel information of each second block. Then, an output frame is obtained according to the noise reduction intensity array, the first frame and the second frame.