Image Restoration Using Grouped Capture Conditions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image restoration processing methods are inefficient for large numbers of images, requiring significant time and often result in noise amplification and ringing effects, making it difficult to obtain high-resolution images.
Innovation Solution
An image processing apparatus that classifies images by capturing conditions, performs common calculation processing to generate optical transfer function or point spread function information, and applies restoration processing using a Wiener filter to reduce noise and enhance image resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image restoration processing is performed on a large number of images using conventional methods, then image resolution is improved, but processing time increases significantly
Solution Approach 1:
The patent segments the image restoration process by classifying images into groups based on capturing conditions (focal length, F-number, object distance). By dividing images into condition-based groups, the system performs restoration processing independently for each group using pre-calculated group-specific parameters, avoiding the need to process each image individually while maintaining restoration quality.
Solution Approach 2:
The patent performs preliminary classification of images into groups based on capturing conditions before restoration processing. Group-specific parameters (optical transfer functions, point spread functions) are pre-calculated for each condition group, enabling efficient batch restoration without recalculating parameters for each individual image, thus reducing overall processing time.
2Reliability
If conventional image restoration processing is applied to multiple images, then image quality is improved, but the processing becomes inefficient and time-consuming
Solution Approach 1:
The patent creates universal restoration parameters at the group level that can be applied to multiple images sharing the same capturing conditions. By establishing group-specific optical transfer functions and point spread functions that represent common characteristics of images under identical conditions, the system achieves multi-functionality where a single set of parameters restores multiple images efficiently while maintaining quality.
Solution Approach 2:
The patent changes the approach from image-by-image parameter calculation to group-based parameter establishment. Restoration parameters are determined based on capturing condition groups rather than individual images, utilizing the statistical characteristics of each group to derive representative parameters that work effectively across multiple images with the same conditions.
3Measurement precision
If standard restoration processing is performed on each image individually, then accurate restoration is achieved, but processing time for large numbers of images becomes excessive
Solution Approach 1:
The patent creates representative restoration parameters by analyzing and copying the common characteristics of images within each condition group. Instead of performing full restoration calculations on every image, the system extracts typical features from group members to establish representative parameters that can be copied and applied to restore all images in that group, significantly reducing computational burden while preserving restoration accuracy.
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
This approach significantly reduces processing time for large numbers of images while minimizing noise and ringing, enabling the generation of high-resolution images by grouping images by capturing conditions and using common parameters for restoration processing.
Implementation Method 1
an image sensor configured to photoelectrically convert an optical image formed via an image pickup optical system to output image data
Data Source
AI summary
An image processing apparatus includes a classifier (804a) which classifies a plurality of captured images into groups for each image capturing condition, a calculation processor (804b) which performs common calculation processing on a plurality of images classified into the same group and generate optical transfer function information or point spread function information for each image capturing condition, and an image restorer (804c) which performs restoration processing on the plurality of images based on the optical transfer function information or the point spread function information.