Mobile Camera Image Processing for Blur Correction
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Solution Overview
Problem
Mobile phone auxiliary cameras face challenges in detecting and correcting camera shake blur due to their miniaturized design and lack of photoflash, leading to increased likelihood of camera shake blur, and existing methods require special devices or inefficient repeated processing steps.
Innovation Solution
An image processing method that detects edges in multiple directions, obtains edge characteristic amounts, and determines blur information, including direction and width, to categorize and correct blurs without requiring additional devices, using isotropic or directional correction parameters based on the type of blur, and adjusts processing speed and accuracy accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If device attachment is used to obtain camera shake information, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The imaging system uses its own existing components (image sensor, processor) to detect and analyze camera shake by examining edge characteristics in the captured image, eliminating the need for external sensors or devices. The system serves itself by deriving shake information from the image data already being collected.
Solution Approach 2:
The patent replaces mechanical sensor-based detection (acceleration sensors, gyroscopes) with an optical/image-processing-based approach. By analyzing edge characteristics and their variations in the captured image, the system substitutes physical measurement devices with computational analysis of visual data.
2Manufacturing precision
If repeated processing steps are used for image restoration, then manufacturing precision is improved, but productivity decreases
Solution Approach 1:
The system performs preliminary analysis of edge characteristics in the original image to predict the type and magnitude of camera shake before restoration processing. By determining the deterioration function in advance based on edge analysis, the system avoids repeated trial-and-error restoration steps, achieving both high quality and efficient processing.
Solution Approach 2:
The system uses feedback from edge characteristic analysis to guide the restoration process. By continuously monitoring edge sharpness and directionality, the system adjusts restoration parameters dynamically, achieving high-quality restoration in fewer processing iterations compared to methods without feedback guidance.
Data Source
AI summary
An analyzing means first calculates the blur direction and blur level of a digital photograph image based on edge widths and histograms of the edge widths obtained in each direction, and discriminates whether the image is a blurred image or a normal image. Then, it further calculates the camera shake level and blur width for the image discriminated to be a blurred image. A parameter setting means sets a one-dimensional correction mask and a two-dimensional correction mask based on the blur width. It also sets a correction level based on the blur level. It further makes an adjustment to the ratio between the one-dimensional and two-dimensional correction masks based on the camera shake level.