Ghost Artifact Removal in HDR Imaging via Difference Mask Segmentation
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Solution Overview
Problem
High-dynamic-range (HDR) images captured with multiple exposures often suffer from ghost artifacts due to movement, which are semi-transparent images of moving objects trailing behind, causing image quality issues.
Innovation Solution
A method and device for removing ghost artifacts by generating and segmenting a difference mask, determining lower and upper thresholds, and creating a refined mask to produce a corrected image through a weighted sum of the original and multiple-exposure images, using a microprocessor and memory in a camera system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If multiple-exposure imaging is used to capture HDR images, then the dynamic range of luminosity is improved, but ghost artifacts appear due to movement between exposures
Solution Approach 1:
The image is divided into multiple exposure captures (first exposure image and second exposure image) to handle different luminance ranges. This segmentation allows the system to capture both dark and bright regions effectively, resolving the contradiction between extended dynamic range and image quality by processing each exposure segment separately and combining them with artifact removal.
2Productivity
If a single exposure time is used, then image capture is simple and fast, but the dynamic range of luminosity is limited
Solution Approach 1:
The system performs preliminary capture of multiple exposure images at different exposure times before combining them. By pre-capturing both a first exposure image (optimized for darker regions) and a second exposure image (optimized for brighter regions), the system enables subsequent HDR processing to achieve extended dynamic range while maintaining capture efficiency through automated multi-exposure sequencing.
3Illumination intensity
If multiple exposure images are combined to form HDR image, then dynamic range is extended, but ghost artifacts from movement are introduced
Solution Approach 1:
The system applies local quality assessment by evaluating pixel-level differences between the first and second exposure images. By calculating absolute differences and comparing them against thresholds, the system identifies regions affected by movement (ghost artifacts) and selectively processes only those regions, preserving accurate regions while removing artifacts from moving regions. This local differentiation resolves the contradiction between extended dynamic range and image accuracy.
4Manufacturing precision
If ghost artifact removal processing is applied, then image quality is improved, but processing complexity increases
Solution Approach 1:
The system employs parameter-based processing by establishing absolute difference thresholds to distinguish between noise and ghost artifacts. By changing the processing parameters (threshold values) based on the calculated absolute differences between exposure images, the system automatically adapts the artifact removal strength without requiring complex manual intervention. This parameter-driven approach improves image quality while controlling processing complexity through automated threshold-based decision making.
Data Source
AI summary
A method for removing a ghost artifact from a multiple-exposure image of a scene method includes steps of generating and segmenting a difference mask, determining a lower threshold and an upper threshold, generating a refined mask, and generating a corrected image. The difference mask includes a plurality of absolute differences in luminance-values between the multiple-exposure image and a first image of the scene. The segmenting step involves segmenting the difference mask into a plurality of blocks. The lower and upper thresholds are based on statistical properties of the blocks. The method generates the refined mask by mapping each absolute difference to a respective one of a plurality refined values, of the refined mask, equal to a function of the absolute difference, the lower threshold, and the upper threshold. The corrected image is a weighted sum of the first image and the multiple-exposure image, weights being based on the refined mask.