Electronic Device Integral Image Noise Reduction
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Solution Overview
Problem
Temporal noise reduction (TNR) in image processing often causes ghosting and dragging when objects move, and requires significant memory, leading to high hardware costs and compromised image quality due to compression or downsampling techniques.
Innovation Solution
The electronic device employs a software-based image processing method that uses integral images to reduce computation by dividing the image into regions of interest and non-interest, selectively performing TNR operations, and separates high-frequency and low-frequency components to minimize computational burden and prevent image distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If temporal noise reduction (TNR) is applied to reduce noise in images, then image quality is improved, but computational complexity and memory requirements increase significantly
Solution Approach 1:
The patent divides the image into multiple regions (first region and second region) and applies different processing methods to each region. The first region uses integral image operations for low-frequency component extraction, while the second region uses standard TNR, thereby reducing overall computational complexity while maintaining image quality.
Solution Approach 2:
The patent applies different noise reduction strategies to different regions of the image based on their characteristics. By identifying regions with moving objects and applying appropriate processing, the patent maintains high image quality in static regions while avoiding ghosting in moving regions.
2Manufacturing precision
If temporal noise reduction (TNR) is applied to reduce noise in images, then image quality is improved, but memory requirements increase
Solution Approach 1:
The patent extracts only the low-frequency component from the image using integral image operations, rather than processing the entire image data. This extraction approach reduces memory requirements by focusing computation on the essential noise-relevant components.
3Device complexity
If integral image operation is performed to extract low-frequency component, then computational load is reduced, but processing time increases
Solution Approach 1:
The patent performs integral image operations and low-frequency component extraction as preliminary steps before applying TNR. By preparing the low-frequency component in advance, the subsequent noise reduction process becomes more efficient and requires less computation time.
Data Source
AI summary
An electronic device comprising a processing unit and a memory that stores a plurality of program instructions. The processing unit executes the program instructions to perform the following steps: (a) storing pixel data of multiple pixels of a picture in the memory, the number of the pixels being greater than the number of pixels in one horizontal line of the picture; (b) performing an integral image operation on the pixel data to obtain integral image data; (c) storing the integral image data in the memory; (d) using the integral image data to calculate a low-frequency component of a target pixel of the picture; and (e) based on the low-frequency component, selectively performing a temporal noise reduction operation on the target pixel.