Image Contrast Enhancement via Selective Local Processing
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Solution Overview
Problem
Existing image processing techniques struggle to enhance contrast in high dynamic range images without losing details in highlights and shadows, often resulting in artifacts or reducing the appeal of the image, especially when applying local contrast enhancement indiscriminately.
Innovation Solution
A method involving down-sampling luminance components, applying a low-pass filter, partitioning the image into sub-images, generating and selecting transforms to map pre-filtered luminance values to post-filtered values, and applying these transforms to the input image, while determining if local contrast enhancement is beneficial based on darkness and redness features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If local contrast enhancement is applied to HDR images, then shadow and highlight details are improved, but artifacts are introduced and image appeal is reduced
Solution Approach 1:
The image is divided into multiple local regions, and contrast enhancement is applied selectively to each region based on its characteristics. This segmentation allows the system to enhance shadow and highlight details in appropriate areas while avoiding artifact introduction in regions where it would be harmful, thus resolving the contradiction between detail visibility and artifact generation.
Solution Approach 2:
Different processing strategies are applied to different regions of the image based on local characteristics. The system identifies regions that benefit from contrast enhancement versus regions where enhancement would create artifacts, and applies appropriate processing to each. This local quality approach ensures detail improvement where needed while maintaining image appeal where enhancement would be harmful.
2Object-generated harmful factors
If bilateral filtering is used to avoid artifacts, then image quality is improved, but computational complexity increases
Solution Approach 1:
The image processing is segmented into multiple stages: initial contrast enhancement, followed by selective bilateral filtering only in regions where artifact reduction is needed. This segmentation reduces overall computational complexity compared to applying bilateral filtering to the entire image, while still achieving artifact reduction where necessary.
Solution Approach 2:
Instead of applying bilateral filtering to the entire image (excessive action), the system applies it only to specific regions where artifact reduction is beneficial (partial action). This selective application reduces computational complexity while maintaining the artifact reduction benefits in critical areas.
3Device complexity
If global histogram transformation is applied, then processing simplicity is maintained, but local detail enhancement is insufficient
Solution Approach 1:
The processing approach is segmented into a simple global histogram transformation step followed by selective local processing in regions that require detail enhancement. This segmentation maintains the simplicity of global processing for most areas while adding targeted local enhancement only where needed, thus resolving the contradiction between processing simplicity and local detail enhancement capability.
Data Source
AI summary
A method and system for contrast enhancement of an input image that utilizes luminance values of pixels of the input image to derive transforms. The method down-samples and partitions an image into sub-images whereby transforms are generated for selected sub-images. The selected sub-image transforms are used to generate an output transform which is applied to the input image for local contrast enhancement of the image. Furthermore, a decision method as to whether or not the input image is to receive local contrast enhancement wherein darkness features of the input image are compared to threshold values and combined into an expression which results in the determination.