Multi-Channel Image Denoising for Real-Time ISP Processing
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Solution Overview
Problem
Conventional image noise reduction algorithms struggle to achieve effective noise reduction while meeting the real-time requirements of ISP chips, with neural-network-based methods being computationally intensive and conventional methods having poor noise reduction effects, especially in low-light environments.
Innovation Solution
A simplified image noise reduction model with a cascaded down-sampling and up-sampling structure, utilizing a U-Net network, that directly processes pixel values of multiple channels simultaneously, reducing computational load and enhancing noise reduction efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural-network-based noise reduction methods are used, then noise reduction effect is improved, but computational complexity increases and real-time performance deteriorates
Solution Approach 1:
The patent segments the image processing into multiple channels (Y channel for brightness, UV channels for color) and processes them through different network paths. The Y channel undergoes down-sampling and up-sampling operations while UV channels are processed separately, then fused together. This segmentation reduces the overall computational burden by dividing the complex denoising task into manageable sub-tasks that can be executed in parallel.
Solution Approach 2:
The patent transforms the image data from spatial domain to frequency domain through down-sampling operations, processes the features in this transformed dimension, and then reconstructs the image through up-sampling. This dimensional transformation allows the network to capture noise patterns more effectively while reducing computational complexity compared to direct spatial domain processing.
2Measurement precision
If complex noise reduction algorithms are used, then noise reduction effect is improved, but processing time increases and real-time performance deteriorates
Solution Approach 1:
By segmenting the processing into parallel channel operations (Y channel and UV channels processed separately then fused), the patent enables simultaneous execution of multiple processing streams, effectively reducing total processing time while maintaining comprehensive noise reduction across all image components.
Solution Approach 2:
The patent applies down-sampling operations that reduce the spatial dimensions of the input image before processing, thereby reducing the total number of computations required. The network processes a down-sampled version of the image and then reconstructs it through up-sampling, achieving acceptable noise reduction with fewer computational operations than full-resolution processing.
3Productivity
If conventional noise reduction algorithms are used, then processing speed is improved, but noise reduction effect deteriorates
Solution Approach 1:
The patent replaces conventional mechanical filtering algorithms with a neural network-based system that uses learned features and patterns. The network is trained to recognize and remove noise while preserving image details, providing superior noise reduction效果 compared to traditional algorithms while maintaining real-time processing capability through optimized architecture.
Solution Approach 2:
By transforming the image to frequency domain through down-sampling and processing features in this transformed space, the network achieves better noise reduction performance than spatial domain algorithms while maintaining computational efficiency. The frequency domain processing allows for more effective separation of noise patterns from image content.
4Measurement precision
If multi-channel processing is implemented, then noise reduction effect is improved, but device complexity increases
Solution Approach 1:
The patent segments multi-channel processing into distinct processing paths for the Y channel (brightness) and UV channels (color), each with appropriate down-sampling and up-sampling operations. The segmented paths are then fused together, managing the complexity of multi-channel processing by organizing it into modular, independent processing streams that can be implemented and optimized separately.
Data Source
AI summary
Disclosed are an image noise reduction processing method and apparatus, a device, a storage medium, and a program product. The method comprises: inputting target image data into an image noise reduction model to obtain noise-reduced image data, the target image data comprising pixel values of each channel of the target image; wherein the image noise reduction model comprises a down-sampling model, an up-sampling model and an output layer that are cascaded, the down-sampling model comprises n cascaded down-sampling modules, and the up-sampling model comprises n cascaded up-sampling modules that are in one-to-one correspondence with the n down-sampling modules; the down-sampling modules comprise a first down-sampling module, a second down-sampling module, and a fusion module cascaded with the first down-sampling module and the second down-sampling module; the first down-sampling module comprises a first convolution layer and a first down-sampling layer, and the second down-sampling module comprises a second down-sampling layer.