Image Processing Apparatus Correcting Luminance via High SNR Pixels
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Solution Overview
Problem
Existing image processing methods struggle to effectively remove noise from images captured in dark scenes without compromising color reproducibility and resolution, especially when using imaging pixels with low signal-to-noise ratios (SNR).
Innovation Solution
An image processing apparatus and method that selects target pixels from images captured by imaging pixels with low SNR and corrects their luminance values based on corresponding pixels or interpolated images from pixels with higher SNR, specifically using RGB imaging pixels arranged on the same layer or stacked, to enhance noise removal while maintaining image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If imaging is performed in dark scenes with increased sensitivity, then noise increases, but image quality deteriorates
Solution Approach 1:
The imaging element is divided into two distinct types of imaging pixels: first imaging pixels with lower SNR and second imaging pixels with higher SNR. This segmentation allows the system to capture images with both types of pixels simultaneously, then use the high-SNR pixels to correct noise in the low-SNR pixels through luminance value correction, thereby resolving the contradiction between increased sensitivity and noise reduction.
Solution Approach 2:
The patent changes the parameter of signal-to-noise ratio by utilizing two different types of imaging pixels with different SNR characteristics. By capturing images with both pixel types and correcting luminance values based on the high-SNR pixels, the system achieves improved noise removal while maintaining the sensitivity required for dark scene imaging.
2Measurement precision
If noise is removed from images captured in dark scenes, then color reproducibility and resolution improve, but existing techniques fail to achieve effective noise removal
Solution Approach 1:
The second imaging pixels with higher SNR serve as an intermediary to correct the luminance values of the first imaging pixels with lower SNR. By using the high-SNR pixels as a reference, the system can effectively remove noise from dark scene images while preserving color reproducibility and resolution, overcoming the limitations of existing noise removal techniques.
Solution Approach 2:
The imaging element combines two different types of imaging pixels (first imaging pixels and second imaging pixels) with different SNR characteristics into a single composite structure. This composite approach allows the system to leverage the strengths of both pixel types, achieving effective noise removal while maintaining image quality in dark scenes.
Data Source
AI summary
An image processing apparatus including a processor is provided. The processor inputs, from an imaging element in which first imaging pixels having a lower SNR and second imaging pixels having a higher SNR are arranged on a same layer, a first captured image by the first imaging pixels and a second captured image by the second imaging pixels when the first imaging pixels and the second imaging pixels perform imaging simultaneously, selects a target pixel from the first captured image, extracts, from the second captured image or an interpolated image of the second captured image, pixels having luminance values close to a luminance value of a pixel corresponding to the target pixel in the second captured image or interpolated image, selects pixels corresponding to the extracted pixels from the first captured image, and corrects a luminance value of the target pixel based on luminance values of the selected pixels.