Image Deblurring Processor Using Class-Specific Filter Selection
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Solution Overview
Problem
Existing electronic devices face challenges in real-time deblurring of image data due to physical characteristics of the device or camera module, which cannot be addressed by recapturing images and results in low operation speed and latency.
Innovation Solution
An electronic device equipped with a processor that identifies classes of image data and selects corresponding filters to restore the image data, using a lookup table for efficient deblurring, allowing for high-speed and real-time processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning-based methods are used for deblurring, then deblurring quality is improved, but processing speed and latency deteriorate
Solution Approach 1:
The patent segments the deblurring process by classifying image data into different types (e.g., motion blur, defocus blur, optical aberrations) and applying specific deblurring algorithms tailored to each class. This segmentation allows the system to avoid using computationally intensive deep learning methods for all images, instead reserving them only for complex cases that require high deblurring quality, thereby improving processing speed while maintaining quality where needed.
Solution Approach 2:
The patent changes the parameter of processing complexity by introducing a classification mechanism that determines the appropriate deblurring approach based on image characteristics. By dynamically adjusting the processing level (from simple algorithmic methods to complex deep learning) based on the classified blur type, the system optimizes the balance between deblurring quality and processing speed, avoiding unnecessary computational overhead for straightforward cases.
2Measurement precision
If recapturing image data is performed to overcome blurring, then image quality is improved, but time consumption increases
Solution Approach 1:
The patent applies preliminary action by implementing a classification system that identifies the type of blur present in captured images before processing. This preliminary classification enables the system to immediately select and apply the most appropriate deblurring method, eliminating the need to discard and recapture images. By preparing multiple deblurring algorithms in advance and selecting based on classification, the system resolves blur issues in real-time without time loss from recapturing.
3Ease of operation
If general deblurring algorithms are applied to all image data, then processing simplicity is maintained, but deblurring effectiveness deteriorates
Solution Approach 1:
The patent implements local quality by applying different deblurring strategies to different classes of image data based on their specific blur characteristics. Instead of using a uniform approach, the system classifies images into categories (such as motion blur, defocus, optical aberrations) and applies specialized algorithms to each class. This localized approach maintains processing simplicity through automated classification while significantly improving deblurring effectiveness by matching the right algorithm to the right image type.
Data Source
AI summary
An electronic device having a camera module for generating image data, a processor for receiving the image data and generating image data restored based on the image data. The electronic device also having a display module displaying the restored image data. The processor being configured to identify a class of the image data, select a filter corresponding to the class, and generate the restored image data by applying the selected filter to the image data.