Image Processing Apparatus for Low-Light Noise and Motion Blur Reduction
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Solution Overview
Problem
Image quality degradation occurs under low-light conditions due to noise and motion blur, especially when using digital cameras, and existing noise removal and motion blur technologies are inadequate for multiple images.
Innovation Solution
An image processing method and apparatus that searches for matching areas between multiple input images, generates hierarchical image structures, and repeatedly performs image processing based on pixel value similarities to produce an output image with improved quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If a long exposure time is set to have a sufficient amount of light exposure for photography, then the light exposure amount is improved, but motion blur occurs due to movement of the image capturing apparatus or an object
Solution Approach 1:
The patent divides the image processing into multiple stages: capturing multiple images with different exposure times, performing initial noise removal on each image, then conducting secondary noise removal by comparing and synthesizing corresponding pixels across multiple images. This segmentation allows the system to benefit from both long exposure (more light) and short exposure (less motion blur) images.
Solution Approach 2:
The patent performs preliminary noise removal processing on each captured image before combining them. By pre-processing individual images to remove noise and motion blur artifacts, the subsequent image combination produces higher quality results with reduced overall noise and minimal motion blur effects.
2Illumination intensity
If photography is performed by setting a camera to a high sensitivity, then the light exposure is improved, but noise increases in an image as a whole
Solution Approach 1:
The patent captures multiple images at the same high sensitivity setting and combines them through pixel-by-pixel comparison and synthesis. By merging multiple images taken under identical high-sensitivity conditions, the system maintains the benefit of sufficient light exposure while reducing noise through the averaging effect of combining multiple samples.
Solution Approach 2:
The patent creates multiple copies of the same scene by capturing multiple images in rapid succession at high sensitivity. These copies are then processed individually and combined, allowing the system to utilize the high sensitivity setting while mitigating noise through statistical averaging across multiple copies.
3Manufacturing precision
If technology of synthetically processing a plurality of images is used, then image quality is improved, but processing complexity increases
Solution Approach 1:
The patent segments the complex image processing task into distinct modules: first noise removal processing for individual images, matching area determination between images, second noise removal processing by combining images, and final output generation. This modular segmentation makes the complex processing more manageable and efficient.
Solution Approach 2:
The patent performs preliminary processing on individual images (noise removal, feature detection) before combining them. By pre-processing each image to remove obvious noise and identify matching areas, the subsequent combination process becomes simpler and more efficient, reducing the overall computational complexity.
Data Source
AI summary
An image processing method includes searching for areas matching each other between a plurality of input images, the plurality of input images comprising a reference image and a target image, generating hierarchical image structures formed of a plurality of hierarchical images having different resolutions with respect to the matching areas between the plurality of input images, repeatedly performing image processing based on a similarity of values of pixels matching each other in the plurality of hierarchical images of the same level by using the hierarchical image structures generated with respect to the matching areas, and generating an output image by using pixel values according to the repeated image processing.