Noise Estimation for Images Using Texture Block Removal
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Solution Overview
Problem
Existing image noise estimation methods inaccurately detect noise due to texture content, leading to erroneous de-noising, as they fail to distinguish between noise and texture, especially in images with high texture content.
Innovation Solution
The method extracts sample blocks of pixels from an image, removes blocks with texture content by identifying edges and high variance, and clusters remaining blocks into color bins to determine average color variances for accurate noise estimation, using data structures like heaps to sort and store blocks based on variance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing noise estimation methods are used on images with texture content, then noise detection is performed, but the detection accuracy deteriorates due to confusion between texture and noise
Solution Approach 1:
The image is divided into multiple sample blocks that are processed independently. Each block is evaluated for texture content separately, and noise estimation is performed on blocks identified as non-texture. This segmentation allows the system to isolate texture-free regions for accurate noise measurement without being influenced by texture content in other parts of the image.
Solution Approach 2:
The method extracts and removes sample blocks containing texture content from the set of blocks used for noise estimation. By identifying blocks with edges or high variance indicative of texture and excluding them, the system separates texture content from the noise estimation process, ensuring that only pure noise signals are measured.
2Device complexity
If all sample blocks are used for noise estimation, then processing is simplified, but measurement accuracy deteriorates due to inclusion of texture content
Solution Approach 1:
Before performing noise estimation, the method performs preliminary classification of sample blocks to identify and exclude those containing texture content. This preliminary action of filtering out texture blocks ensures that the subsequent noise estimation is performed only on suitable blocks, maintaining measurement precision without requiring complex post-processing corrections.
Solution Approach 2:
The method introduces an intermediary classification step that acts as a mediator between the raw image blocks and the noise estimation process. This intermediary layer evaluates each block for texture content and selectively passes only non-texture blocks to the noise estimator, thereby protecting the measurement accuracy from texture interference while maintaining processing efficiency.
Data Source
AI summary
Implementations relate to estimating noise in images. In some implementations, a method includes extracting a plurality of sample blocks of pixels from a received image, where each sample block includes a subset of pixels of the image. One or more of the sample blocks are examined for texture content based on whether the sample blocks include one or more edges based on a predetermined threshold. At least one sample block determined to include texture content is removed. The method determines one or more average color variances based on the remaining sample blocks that have not been removed, where noise estimations for the image are based on the average color variances.