Image Noise Reduction via Adaptive Cyclic Coefficient Adjustment
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Solution Overview
Problem
Existing image processing techniques struggle to effectively reduce the unpleasant impression of motion in composite images generated from moving images shot under different exposure conditions, leading to ghosting and subject blur, especially when compositing short and long exposure images or images of different wavelength ranges.
Innovation Solution
An image processing apparatus and method that determine a cyclic coefficient based on both inter-frame change amount and exposure period to apply cyclic noise reduction processing, adjusting the composition rate to minimize the impact of moving subjects and reduce noise, thereby alleviating ghosting and subject blur in wide dynamic range composite images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If standard WDR composition processing is applied to composite short and long exposure images, then a wide dynamic range composite image is generated, but ghosting and subject blur occur throughout a wide range in the composite image when moving subjects are present
Solution Approach 1:
The patent applies different cyclic coefficients to different regions of the image based on motion detection. For pixels identified as moving subjects, a first cyclic coefficient is used, while for stationary background pixels, a second cyclic coefficient is applied. This local differentiation allows the system to reduce ghosting in moving subject regions while maintaining proper exposure dynamics in stationary regions, thereby improving overall image quality without sacrificing dynamic range.
Solution Approach 2:
The patent dynamically adjusts the cyclic coefficient based on the detected motion state of each pixel region. By calculating the degree of change between frames and determining whether pixels belong to moving or stationary objects, the system adaptively modifies the composition rate. This dynamic adjustment enables the processing to respond to scene changes in real-time, reducing ghosting artifacts while preserving the intended wide dynamic range effect.
2Manufacturing precision
If cyclic noise reduction processing with a fixed cyclic coefficient is applied, then noise is reduced in the composite image, but moving subjects cannot be properly distinguished from the background
Solution Approach 1:
The patent implements spatially-varying cyclic coefficients where moving subject regions and stationary background regions are treated differently. By identifying moving pixels through frame-to-frame change detection, the system applies appropriate noise reduction strength to each region. This local quality approach ensures that noise is reduced in stationary regions while preserving the visibility and temporal characteristics of moving subjects.
Solution Approach 2:
The system dynamically determines the cyclic coefficient for each pixel based on its motion state. Pixels that change between frames (moving subjects) receive a different cyclic coefficient compared to stationary pixels. This dynamic adaptation allows the noise reduction processing to differentiate between noise and actual motion, preventing the loss of moving subject information while still achieving effective noise reduction in stable regions.
3Manufacturing precision
If a technique that determines whether each pixel is a moving subject region is used, then ghosting can be reduced, but calculation cost increases and circuit scale becomes large
Solution Approach 1:
The patent performs motion detection and cyclic coefficient determination selectively rather than uniformly across all pixels. By focusing processing on regions where motion is detected or where it significantly impacts composite quality, the system achieves effective ghosting reduction without the excessive calculation cost of processing every pixel with full complexity. This partial action approach balances performance with computational efficiency.
Solution Approach 2:
The patent simplifies the complexity by changing the parameter representation and processing approach. Instead of complex object-based tracking, the system uses pixel-level change detection with simplified metrics to determine motion states. This parameter change from complex object recognition to simpler pixel difference analysis reduces calculation cost and circuit scale while still achieving effective ghosting reduction through the resulting cyclic coefficient adjustments.
Data Source
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AI summary
An image processing apparatus (403, 501) comprises: obtaining means (601) configured to obtain a plurality of image signals being composed of image signals shot under different shooting conditions; determination means (604) configured to determine a composition rate in order to composite an image signal that has the predetermined shooting condition with a noise-reduced image that has the predetermined shooting condition, in accordance with an inter-frame change amount and a parameter indicating the predetermined shooting condition; noise reduction means (605) configured to composite the image signal with the noise-reduced image using the composition rate to generate a new noise-reduced image; and composition means (605) configured to composite the new noise-reduced image and an image signal that has another shooting condition.