Image Processing Device Multi-Frame Noise Reduction Blurring
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Solution Overview
Problem
Existing multi-frame noise reduction (MFNR) techniques often fail to effectively reduce blurring in images, as they use images with blurring as references, leading to residual blurring in the final composite image.
Innovation Solution
The proposed solution involves selecting an image with minimal blurring as the reference and sorting subsequent images by blurring amount for superimposition, using inertial sensor data to calculate an index of blurring and aligning images for combination, thereby reducing blurring and noise in the final image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the first image of a plurality of images continuously captured is used as the reference image for superimposition, then the processing is simple, but blurring remains in the final image when the first image has blurring
Solution Approach 1:
The patent applies preliminary action by evaluating the blurring degree of each image before selecting the reference image. The control unit calculates an index indicating the degree of blurring for each image based on inertial sensor information, and selects the image with the smallest index as the reference image. This preliminary evaluation ensures that the reference image is chosen to minimize blurring in the final composite image, rather than simply using the first image captured.
2Ease of operation
If images are superimposed in the order of capture, then the processing is straightforward, but blurring and noise are not effectively reduced
Solution Approach 1:
The patent applies parameter changes by altering the superimposition order based on the blurring degree parameter. Instead of superimposing images in capture order, the control unit sorts the images in ascending order of their blurring indices and superimposes them in this sorted order. This parameter-based sorting ensures that images with less blurring contribute more to the final composite, thereby effectively reducing both blurring and noise while maintaining a systematic processing approach.
3Illumination intensity
If a slow shutter speed is used to capture brighter images, then image brightness is improved, but camera shake occurs or moving subjects are blurred
Solution Approach 1:
The patent applies merging by combining multiple images captured at slow shutter speeds into a single composite image through superimposition. By selecting a reference image with minimal blurring and superimposing other images in ascending order of their blurring degrees, the system effectively merges the brightness benefits of slow shutter speeds while reducing the blurring effects through the selective combination process.
4Illumination intensity
If high ISO sensitivity is set to capture brighter images, then image brightness is improved, but noise increases
Solution Approach 1:
The patent applies merging by combining multiple images captured at high ISO sensitivity levels. By superimposing these images in ascending order of their blurring indices, the system averages out the noise present in individual high-ISO images while maintaining the brightness advantage, thereby reducing the harmful noise effect while preserving image brightness.
Data Source
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AI summary
Provided is an information processing apparatus capable of further enhancing an effect of reducing blurring or noise in an image at the time of MFNR. An information processing apparatus including a control unit that selects a reference image from a plurality of images continuously captured by an image capturing apparatus, on the basis of information of the image capturing apparatus acquired by an inertial sensor when each of the images is captured, and superimposes remaining images on the reference image while performing alignment with the reference image, to combine the images into one image.