Tile-Based Image Contrast Enhancement with Bias and Plateau Histograms
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Solution Overview
Problem
Existing image processing techniques, such as histogram equalization, often result in distracting artifacts or noise, particularly when enhancing images with limited dynamic range, leading to loss of details in shadows and bright areas, and can overly alter image brightness, compromising the natural appearance of the image.
Innovation Solution
The method involves dividing images into tiles, constructing and applying bias and plateau values to histograms, and convolving cumulative distribution functions (CDFs) across adjacent tiles to enhance pixel intensity values, thereby preventing overstretching and preserving image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If histogram equalization is applied to enhance image contrast, then the distribution of gray level values becomes uniform, but distracting artifacts and noise are introduced, particularly in images with limited dynamic range
Solution Approach 1:
The image is divided into multiple tiles, and separate histograms are constructed for each tile. This segmentation allows localized contrast enhancement without applying global histogram equalization, thereby reducing artifacts and noise while maintaining detail in shadows and bright areas.
Solution Approach 2:
Different regions of the image (tiles) are processed independently with their own cumulative histograms. This enables local contrast enhancement that adapts to the specific intensity distribution of each region, preventing overstretching and preserving natural appearance while enhancing contrast where needed.
2Loss of information
If histogram equalization is applied to reveal details in shadows and bright areas, then contrast is enhanced, but details are lost in dark areas and overall image quality is compromised
Solution Approach 1:
The image is segmented into tiles with independent histogram processing. This allows detailed enhancement in specific regions (shadows and bright areas) while preserving overall image quality, as each tile's cumulative histogram is constructed and applied independently without compromising other regions.
Solution Approach 2:
The patent applies bias values and plateau values to modify the histogram before constructing cumulative histograms. These parameter adjustments enable controlled contrast enhancement that reveals details while maintaining image quality, preventing the excessive brightness alteration that would compromise reliability.
3Manufacturing precision
If pixel values are remapped to take advantage of full dynamic range, then contrast enhancement is achieved, but image brightness is overly altered and natural appearance is compromised
Solution Approach 1:
Each tile receives localized contrast enhancement based on its specific intensity distribution. The cumulative histogram for each tile is constructed independently and applied only to that region, allowing contrast improvement without excessive brightness alteration that would compromise natural appearance.
Solution Approach 2:
Bias values are applied to adjust the histogram before cumulative histogram construction. This parameter change enables controlled remapping of pixel values that enhances contrast while preserving natural brightness characteristics, preventing overly altered appearance.
4Device complexity
If the image is processed as a single unit with global histogram equalization, then processing is simple, but contrast enhancement is limited and details in specific regions are lost
Solution Approach 1:
The image is divided into multiple tiles, and separate cumulative histograms are constructed for each tile. This segmentation enables detailed enhancement in specific regions while the processing remains manageable through systematic tile-based processing, balancing complexity with improved detail preservation.
Solution Approach 2:
Instead of applying full histogram equalization to the entire image, the patent applies partial enhancement to each tile using its own cumulative histogram. This partial action preserves details in specific regions without the excessive transformation that would occur with global equalization, maintaining natural appearance.
Data Source
AI summary
A method of enhancing an image. This includes the steps of: dividing the image into multiple tiles and constructing a histogram for each tile, wherein the histogram represents a distribution of pixel intensity values in each tile. In addition, the method applies a bias value to the histogram and a plateau value to the histogram. A cumulative histogram is constructed after applying the bias value and the plateau value to the histogram. The method transforms each pixel in the image, by weighing four cumulative histograms, respectively, in four adjacent and closest tiles to the pixel under consideration. The pixel under consideration is modified based on a weighted cumulative histogram.