Multi-Frame Image Fusion Weights for Resolution and Noise
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Solution Overview
Problem
Existing image processing methods require significant computational resources, making them slow and inefficient for enhancing image resolution and signal-to-noise ratio, especially when using camera modules with low performance specifications.
Innovation Solution
A method that involves receiving multiple frames of an image, selecting a main frame and aligning reference frames with it, determining fusion weights by comparing the main frame and reference frames, and obtaining a weighted combination of the frames to enhance image quality, while reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing methods are used to enhance resolution and signal-to-noise ratio, then image quality is improved, but computational resources required increase significantly making the process slow
Solution Approach 1:
The patent segments the image processing task by dividing multiple captured frames into separate reference frames and a main frame. Each reference frame is processed independently through alignment and fusion weight determination, then combined with the main frame. This segmentation allows parallel processing and reduces the computational burden on any single operation, thereby improving processing speed while maintaining image quality enhancement.
Solution Approach 2:
The patent performs preliminary actions by capturing multiple frames before final processing. These frames are prepared in advance through alignment with the main frame and fusion weight determination. By performing these actions preliminarily on separate frames rather than processing the entire image at once, the system reduces real-time computational complexity and increases processing speed.
2Measurement precision
If conventional image processing methods are used to enhance resolution and signal-to-noise ratio, then image quality is improved, but power consumption increases
Solution Approach 1:
The patent segments the image processing workload across multiple frames, where each frame is processed independently through alignment and fusion operations. This segmentation distributes the computational load over time and resources, reducing peak power consumption compared to processing a single high-resolution image through intensive algorithms. The fused result combines information from multiple lower-cost processing operations.
3Measurement precision
If conventional image processing methods are used to enhance resolution and signal-to-noise ratio, then image quality is improved, but memory usage increases
Solution Approach 1:
The patent segments the image data into multiple frames that are processed and stored separately as reference frames. Each reference frame occupies memory independently, and the fusion weights are calculated and stored as separate data structures. This segmentation allows for more efficient memory management compared to loading and processing a single large high-resolution image, as the memory can be allocated and freed more flexibly for each frame and its corresponding fusion weights.
Data Source
AI summary
A method performed by an electronic device with one or more processors and memory includes receiving a plurality of frames of an image; selecting one frame of the plurality of frames as a main frame thereby leaving the rest of the plurality of frames as reference frames; aligning the reference frames with the main frame; determining fusion weights for a respective reference frame of the reference frames by comparing the main frame and the respective reference frame; and obtaining a weighted combination of the main frame and the reference frames based on the fusion weights. An electronic device configured for performing the method and a computer readable storage medium storing instructions for performing the method are also disclosed.