Block-Wise Tone Mapping Curves for Contrast Enhancement Without Noise Gain
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Solution Overview
Problem
Conventional contrast limited adaptive histogram equalization (CLAHE) often results in excessive noise amplification or reduced contrast enhancement due to manual adjustment of histogram clipping values, limiting its effectiveness in enhancing image quality.
Innovation Solution
An image enhancement method that divides an image into blocks, calculates statistical data, and uses predefined tone mapping curves based on indices to adjust pixel values, reducing computing resources and avoiding noise amplification while improving contrast.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Strength
If histogram clipping value is adjusted to improve image contrast, then contrast enhancement magnitude increases, but image noise amplification increases
Solution Approach 1:
The patent applies dynamics by making the histogram clipping value adaptive rather than fixed. The clipping value is dynamically adjusted based on local image characteristics (standard deviation and mean intensity) of different image blocks. This allows the system to automatically optimize contrast enhancement for each region while avoiding excessive noise amplification, resolving the contradiction between contrast enhancement magnitude and noise amplification.
Solution Approach 2:
The patent divides the image into multiple blocks and applies different histogram clipping values to different blocks based on their local characteristics. Each block's clipping value is determined by its standard deviation and mean intensity, enabling localized contrast optimization. This local quality approach ensures that high-contrast regions receive appropriate enhancement while low-contrast or noisy regions are preserved, resolving the contradiction between overall contrast enhancement and local noise control.
2Strength
If manual adjustment of histogram clipping value is used to control contrast enhancement, then contrast magnitude can be controlled, but computational complexity and manual intervention increase
Solution Approach 1:
The patent implements self-service by enabling the system to automatically determine optimal histogram clipping values without manual intervention. The algorithm calculates the standard deviation and mean intensity of each image block, then automatically selects appropriate clipping values from a predefined set based on these statistical measures. This self-service mechanism eliminates the need for manual parameter adjustment while maintaining effective contrast control, resolving the contradiction between contrast control capability and system complexity.
Solution Approach 2:
The patent applies parameter changes by transforming the fixed histogram clipping value into a dynamic parameter that changes based on image characteristics. Instead of using a single manual clipping value, the system computes local statistical parameters (standard deviation and mean) and maps them to appropriate clipping values. This parameter transformation enables automatic adaptation to different image regions and conditions, resolving the contradiction between controlled contrast enhancement and manual intervention requirements.
Data Source
AI summary
An image enhancement method and an electronic device are disclosed. The image enhancement method includes the following steps. An original image is divided into multiple image blocks. A statistical data of each of the image blocks is calculated, and at least one index is obtained according to the statistical data of the each of the image blocks. A final tone mapping curve of the each of the image blocks is obtained based on multiple predefined tone mapping curves according to the at least one index of the each of the image blocks. Afterwards, the original image is adjusted according to the final tone mapping curve of the each of the image blocks to obtain an enhanced image.