Beta Distribution Tone Mapping for HDR Image Contrast and Artifact Reduction
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Solution Overview
Problem
Existing tone-mapping algorithms struggle to maintain contrast in high dynamic range (HDR) images, leading to hazier images, especially when capturing scenes with highlights like neon lights. Additionally, these algorithms often introduce artifacts such as halo or dark spot artifacts.
Innovation Solution
The proposed solution involves a beta distribution-based global tone mapping method combined with sequential weight generation for tone fusion. This approach generates low dynamic range (LDR) images from HDR images, creates tone-type weight maps, and calculates blending weights to fuse these LDR images effectively, while applying beta distribution-based transforms to enhance contrast without excessive brightness changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If existing tone-mapping algorithms are used to process HDR images, then the images can be converted to LDR format, but contrast is lost and images become hazy
Solution Approach 1:
The patent segments the tone mapping process into multiple stages: generating multiple LDR images from HDR with different exposure levels, creating separate weight maps for different tone types (dark, mid, bright), and sequentially fusing these weighted images. This segmentation allows contrast preservation in different tonal regions without compromising overall image quality.
Solution Approach 2:
The patent applies local quality by generating different weight maps for different tone types (dark tones, mid tones, bright tones) and different exposure levels. Each region of the image receives customized weighting and blending based on its local tonal characteristics, preserving contrast locally while maintaining global image quality.
2Manufacturing precision
If existing tone-mapping algorithms process HDR images with highlights, then conversion to LDR is achieved, but artifacts such as halo or dark spot artifacts are introduced
Solution Approach 1:
The patent performs preliminary actions by generating multiple LDR images from the HDR image at different exposure levels before the final fusion step. Weight maps are pre-computed for each tone type and exposure level, preparing the data in advance to guide the fusion process and prevent artifact formation during tone mapping.
Solution Approach 2:
The patent implements feedback mechanisms through sequential weight generation where weight maps are computed based on the characteristics of LDR images, and these weights are then used to fuse images while preserving highlight details. The process continuously adjusts weights to minimize artifact generation in high-contrast regions.
Data Source
AI summary
A method includes obtaining a high dynamic range (HDR) image and generating low dynamic range (LDR) images based on the HDR image, where at least some of the LDR images are associated with different exposure levels. The method also includes generating tone-type weight maps based on the LDR images, where at least one of the LDR images is associated with two or more of the tone-type weight maps. The method further includes generating blending weights for the LDR images based on the tone-type weight maps, where the blending weights for at least one of the LDR images are based on at least two tone-type weight maps associated with at least two of the LDR images.