Weighted Multi-Kernel Image Processing for Low-Light Noise Reduction
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Solution Overview
Problem
Existing image processing technologies struggle to effectively convert low-quality images, particularly those captured under limited lighting conditions or using low-performance cameras, into high-quality images.
Innovation Solution
A method and system utilizing weighted multiple kernels through a network with two branches for kernel prediction and weight prediction, applying a weighted sum of kernels to input images to generate high-quality images, followed by a super-resolution imaging process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a single kernel is used for image processing, then the device complexity is low, but the image quality and noise reduction performance are insufficient
Solution Approach 1:
The patent segments the kernel processing into multiple independent kernels (first kernel, second kernel, third kernel) with different functions. Each kernel handles specific aspects of image processing (noise reduction, edge preservation, detail enhancement) separately, allowing the system to achieve superior image quality without excessive complexity by dividing the complex task into manageable segments
Solution Approach 2:
The patent creates a universal image processing system that handles multiple image quality requirements simultaneously through multi-kernel processing. The system can adaptively select and combine different kernels based on image characteristics, making it versatile for various scenarios (low-light, noisy, high-detail images) without requiring separate specialized models for each case
2Manufacturing precision
If multiple kernels are used for image processing, then the noise reduction and image quality improve, but the processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary actions by pre-defining multiple kernels with specific functions before actual image processing. The kernels are prepared in advance with optimized parameters for different processing tasks, so during runtime the system only needs to select and apply the appropriate pre-prepared kernels rather than computing them from scratch, significantly reducing processing time
Solution Approach 2:
The patent applies partial action by selectively applying different kernels to different regions or aspects of the image based on local characteristics. Instead of applying all kernels uniformly to the entire image, the system applies only the necessary kernels to specific regions where they are most beneficial, reducing overall computational load while maintaining noise reduction effectiveness
3Manufacturing precision
If weighted multiple kernels are applied, then the image alignment and quality enhancement improve, but the computational resources and processing complexity increase
Solution Approach 1:
The patent implements dynamic kernel selection and weighting based on image characteristics. The system dynamically adjusts which kernels to apply and what weights to assign them based on local image features, noise levels, and content types. This dynamic adaptation allows the system to use minimal computational resources by only activating necessary kernels with appropriate weights for each specific situation, rather than applying all kernels at full strength uniformly
Solution Approach 2:
The patent changes parameters (kernel weights, selection criteria) based on image characteristics to optimize the balance between alignment quality and computational cost. By adjusting kernel parameters dynamically according to image content, the system achieves high alignment precision without consistently requiring maximum computational resources, adapting the processing intensity to match the actual needs of each image region
Data Source
AI summary
Systems and methods for processing a plurality of images include obtaining input data including the plurality of images; providing the input data to a first machine learning model; providing an output of the first machine learning model to a second machine learning model and a third machine learning model; generating a first feature map corresponding to a plurality of kernels based on an output of the second machine learning model; generating a second feature map corresponding to a plurality of weights based on an output of the third machine learning model; generating a predicted kernel based on a weighted sum of the plurality of kernels; and generating output data based on the input data and the predicted kernel.