Iterative Tile Processing for Digital Image Noise Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Digital video streams are affected by noise during generation, transmission, and compression, leading to a reduction in visual quality, and existing methods are inadequate for effectively reducing noise across the image processing pipeline.
Innovation Solution
A method involving iterative image processing operations on tiles within a digital image, where the image processing region decreases in size with each iteration, using a sliding window to compute output values based on differences between corresponding pixels in two images, and adjusting weights based on brightness and reference parameters to reduce noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional noise reduction methods are applied to digital video streams, then noise is reduced to some extent, but visual quality is significantly degraded and processing efficiency is low
Solution Approach 1:
The image is divided into multiple tiles, and each tile is further divided into processing regions that shrink iteratively. This segmentation allows noise reduction to be applied locally with different processing intensities, preserving visual quality while reducing noise. The multi-scale processing regions enable fine-tuned control over which areas receive aggressive noise reduction.
Solution Approach 2:
The patent implements dynamic iteration where the processing region size changes across K iterations. In each iteration, the processing region shrinks based on the previous iteration's results, allowing the algorithm to adaptively focus on different spatial scales. This dynamic approach enables the system to preserve edges and details in early iterations while applying stronger noise reduction in later iterations on smaller regions.
2Object-affected harmful factors
If aggressive noise reduction processing is applied, then noise is reduced effectively, but processing time and computational complexity increase significantly
Solution Approach 1:
By segmenting the image into tiles and further into iterative processing regions, the patent reduces the computational burden compared to processing the entire image uniformly. Each tile can be processed independently and in parallel, and the shrinking processing regions reduce the number of pixels requiring computation in later iterations.
Solution Approach 2:
The patent applies noise reduction selectively to specific processing regions rather than the entire image. The processing region shrinks across iterations, applying full processing only where needed while reducing or eliminating processing in other areas. This partial action approach maintains effective noise reduction while significantly reducing overall processing time.
3Productivity
If simple processing methods are used, then processing speed is fast, but noise reduction effectiveness is insufficient
Solution Approach 1:
The dynamic iterative approach with K iterations and shrinking processing regions provides a progressive refinement strategy. Early iterations use larger processing regions for coarse noise reduction, while later iterations focus on finer details. This dynamic multi-scale approach achieves superior noise reduction effectiveness compared to simple single-pass methods while maintaining reasonable processing speed through efficient region management.
Data Source
AI summary
Methods and systems are provided for processing an image. The method may include selecting one or more tiles in a first image. The method may also include performing, for each of the one or more tiles, an image processing operation on an image processing region within the tile based on a second image for K iterations, decreasing the size of the image processing region with each iteration, wherein K is an integer above 2.