Image Fusion Using Gradient Coefficients for High Dynamic Range Scenes
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Solution Overview
Problem
Current image processing technologies face challenges in capturing details of both highlighted and dark areas in a single photo, especially in high dynamic range scenes like nightscapes, due to camera limitations, leading to suboptimal fusion results.
Innovation Solution
An image fusion method that calculates a fusion coefficient image and gradients of two frame images with different brightness settings, allowing for better fusion by marking fusion weights and reconstructing pixels, resulting in an output image that combines dark and highlight details effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple images with different exposure settings are captured and fused, then detail capture in high dynamic range scenes is improved, but the complexity of the image processing system increases
Solution Approach 1:
The patent divides the image fusion process into distinct segments: gradient calculation for each image, fusion coefficient determination, and final fusion computation. By segmenting the processing pipeline, the system manages complexity through modular operations that can be independently optimized and executed.
Solution Approach 2:
The patent performs preliminary calculations of gradients and fusion coefficients before the actual image fusion. This preliminary action prepares the data structures and computational parameters in advance, reducing the complexity of the final fusion operation and enabling more efficient processing of detailed information.
2Manufacturing precision
If gradient fusion and reconstruction techniques are used, then fusion effect in varying brightness scenes is improved, but computational complexity increases
Solution Approach 1:
The patent applies gradient fusion and reconstruction techniques selectively based on local image characteristics. By analyzing brightness variations and applying sophisticated fusion methods only where needed rather than uniformly across the entire image, the system improves fusion效果 in varying brightness scenes while controlling overall computational complexity.
3Measurement precision
If fusion coefficients are calculated based on multiple images, then fusion accuracy is improved, but processing time increases
Solution Approach 1:
The patent calculates fusion coefficients as a preliminary step before final image fusion. By determining these coefficients in advance based on multiple images, the system achieves accurate fusion results while organizing the computational workload to minimize processing time impact.
Solution Approach 2:
The fusion coefficient calculation leverages self-service mechanisms where the algorithm automatically adapts to local image characteristics without requiring manual intervention or iterative optimization. This enables accurate fusion coefficients to be computed efficiently based on the inherent properties of the input images.
Data Source
AI summary
The present disclosure provides a method, an electronic device, and a medium for image fusion. The method includes calculating a fusion coefficient image M based on a first frame image I1 or based on both the first frame image I1 and a second frame image I2; calculating a first gradient D1 of the first frame image I1 and a second gradient D2 of the second frame image I2; calculating a preliminary fusion result J based on the image M, the first gradient D1 and the second gradient D2; and obtaining an output image I3 based on the image M, the first gradient D1, the second gradient D2 and the preliminary fusion result J, wherein brightness of the first frame image I1 is greater than brightness of the second frame image I2, and wherein the image M is used to mark fusion weights of pixels in the first frame image I1.