Image Processing with Inertia-Guided Motion and Noise Filtering
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Solution Overview
Problem
Existing image processing systems face challenges in efficiently removing noise from images while reducing motion detection time, particularly when the image sensor is in motion.
Innovation Solution
An image processing device and method that utilizes a motion manager to generate movement information based on pixel values and inertia sensor velocity, a similarity map generator to determine a noise removal filter weight, and a noise remover to apply a bilateral filter considering the movement information, effectively removing temporal and spatial noises.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If motion detection is performed by comparing consecutive images, then motion information can be obtained, but the time required for motion detection increases when the image sensor is moved
Solution Approach 1:
The patent segments the image into multiple regions of interest (ROIs) and performs motion detection only on these specific regions rather than comparing entire consecutive images. This segmentation approach reduces the computational burden and time required for motion detection while maintaining accuracy in detecting actual motion patterns in critical areas of the image.
Solution Approach 2:
The patent applies preliminary actions by using inertia sensor data to predict and compensate for sensor movement before performing image processing. By pre-compensating for expected motion based on sensor velocity and acceleration data, the system reduces the time needed for post-capture motion correction and detection.
2Object-affected harmful factors
If noise removal is performed on entire images, then noise can be removed, but computational load increases
Solution Approach 1:
The patent divides the image into multiple regions of interest and applies noise removal algorithms only to these specific regions rather than processing the entire image. This segmentation strategy significantly reduces computational load while maintaining effective noise removal in the most important areas of the image where noise would have the greatest impact on quality.
Solution Approach 2:
The patent applies different processing strategies to different regions of the image based on their importance. High-priority regions receive full noise removal processing, while less critical regions use simplified or no processing. This local quality approach ensures that computational resources are focused on areas where noise removal provides the most benefit to overall image quality.
3Stability of the object's composition
If motion compensation is applied to entire images, then image stabilization is improved, but processing time increases
Solution Approach 1:
The patent applies motion compensation only to specific regions of interest rather than the entire image. By identifying and processing only the critical regions that require stabilization, the system maintains high image stabilization quality in important areas while significantly reducing the overall processing time required for motion compensation across the full image.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution enables rapid acquisition of motion information and efficient noise removal, improving image quality by reducing computational load and enhancing image stabilization.
Implementation Method 1
velocity information of an inertia sensor configured to sense a velocity of the image sensor
Data Source
AI summary
Provided herein may be an image processing device and an image processing method. The image processing device may include a motion manager configured to generate movement information of an image sensor based on pixel values of a first frame image received from the image sensor, pixel values of a second frame image received from the image sensor prior to the first frame image, and velocity information of an inertia sensor configured to sense a velocity of the image sensor, a similarity map generator configured to generate a similarity map indicating a weight of a noise removal filter based on the pixel values of the first frame image, and a noise remover configured to remove temporal and spatial noise based on pixel values of the first frame image to which the noise removal filter is applied based on the similarity map and pixel values of the second frame image.