Neural Network Pre-Processing for CFM Image Noise Suppression
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Solution Overview
Problem
Current image processing techniques for mobile devices face challenges in converting color filter mosaic images into high-quality RGB images due to noise, limited hardware capabilities, and dependency on specific ISP pipelines, which can lead to loss of details and require retraining when modifications are made.
Innovation Solution
An image processing apparatus using a neural network to enhance the quality of color filter mosaic images before applying ISP, allowing for efficient use of resources and independent of specific ISP pipeline implementations, with a pre-processing stage that fine-tunes output quality using neural network parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional ISP pipeline processing is used to convert CFM images to RGB images, then the processing can be performed using standard algorithms, but the image quality deteriorates due to noise amplification, loss of details, and artifacts
Solution Approach 1:
The neural network performs preliminary processing on the CFM image before it enters the conventional ISP pipeline. The network pre-dennoises and pre-processes the image data, so that subsequent ISP operations work on already-cleaned data, preventing noise amplification and detail loss that would otherwise occur during contrast enhancement and other processing steps
Solution Approach 2:
The neural network acts as an intermediary component between the image sensor and the conventional ISP pipeline. It transforms the raw CFM image into a pre-processed version that maintains statistical properties compatible with standard ISP operations while removing harmful noise and artifacts, thus bridging the gap between raw sensor data and high-quality RGB output
2Manufacturing precision
If supervised learning approach with end-to-end neural network is used to transform CFM to RGB images, then image quality can be improved, but the system requires expensive retraining when ISP pipeline modifications are made
Solution Approach 1:
The image processing system is segmented into two independent parts: a neural network for quality enhancement and a conventional ISP pipeline for color space transformation. The neural network processes CFM images and outputs enhanced data that maintains compatibility with standard ISP operations, allowing the ISP pipeline to be modified independently without requiring retraining of the neural network
Solution Approach 2:
The neural network is designed to produce output with statistical properties that are universally compatible with conventional ISP pipelines. By maintaining this compatibility, the solution allows different ISP implementations (Bayer to RGB, demosaicing, white balancing) to work with the neural network output without requiring the network to be retrained for each specific pipeline configuration
3Manufacturing precision
If contrast enhancement is applied during ISP processing, then visual quality can be improved, but noise in the image is amplified
Solution Approach 1:
The neural network performs denoising as a preliminary action before the image undergoes contrast enhancement in the ISP pipeline. By removing noise early in the processing chain, the subsequent contrast enhancement operates on clean data and cannot amplify noise, thus achieving visual quality improvement without the harmful side effect of noise amplification
Data Source
AI summary
An image processing apparatus processes a color filter mosaic, CFM, image of a scene into a final image of the scene. The image processing apparatus includes processing circuitry configured to implement a neural network. The neural network is configured to process the CFM image into an enhanced CFM image. The processing circuitry is further configured to transform the enhanced CFM image into the final image.