Image Processing Apparatus Area-Selective Noise Reduction
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Solution Overview
Problem
Existing image processing techniques, such as area-by-area noise reduction, are limited in applying different noise reduction characteristics to specific areas within an image, leading to uniform noise reduction across the entire image, which does not cater to varying importance on visual recognition and image quality needs.
Innovation Solution
An image processing apparatus comprising a feature extractor, map estimator, first and second image estimators, and an outputter, which extracts intermediate features, estimates area maps, and merges images based on these maps to apply distinct noise reduction processing for character recognition and image quality on a per-area basis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If uniform noise reduction processing is applied to the entire image, then the processing simplicity is maintained, but the image quality in specific areas (such as character areas) cannot be optimized
Solution Approach 1:
The image is segmented into different subject areas using subject distance information, allowing different noise reduction processing to be applied to different regions. This segmentation enables optimized processing for character areas while maintaining simplicity in the overall processing framework.
Solution Approach 2:
Different noise reduction processing parameters are applied to different subject areas based on their specific requirements. Character areas receive processing optimized for visual recognition, while other areas receive processing optimized for image quality, achieving local optimization without complicating the overall system.
2Manufacturing precision
If different noise reduction characteristics are applied to specific areas, then the image quality for visual recognition is improved, but the device complexity increases
Solution Approach 1:
Subject distance information is acquired in advance to identify character areas before noise reduction processing. This preliminary classification allows the system to apply appropriate processing to different areas without adding significant complexity during the actual noise reduction stage.
Solution Approach 2:
The noise reduction processing system is designed to handle multiple types of subject areas using a unified framework. The same basic processing algorithm is applied with different parameters based on the subject area type, avoiding the need for completely separate processing systems for different areas.
3Productivity
If area-by-area noise reduction processing is performed with preset parameters, then the processing efficiency is maintained, but the adaptability to different subject areas is limited
Solution Approach 1:
The noise reduction processing parameters are dynamically changed based on the subject area type. Character areas use parameters optimized for preserving visual recognition characteristics, while other areas use parameters optimized for image quality, allowing the system to adapt to different areas while maintaining processing efficiency through a unified parameter adjustment mechanism.
Data Source
AI summary
An image processing apparatus includes a feature extractor, a map estimator, a first image estimator, a second image estimator, and an outputter. The feature extractor extracts an intermediate feature from an input image. The map estimator estimates an area map from the intermediate feature. The first image estimator estimates a first image from the intermediate feature. The second image estimator estimates a second image from the intermediate feature. The outputter outputs an output image obtained by, based on the area map, merging the first image and the second image. The second image estimator is trained to obtain desired image quality at a particular area based on the area map in the second image.