Image Deconvolution Using Text Feature Recognition
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Solution Overview
Problem
The challenge lies in effectively applying blind image deconvolution on inexpensive digital cameras and mobile devices due to limited computing power, varied imaging environments, and optical limitations, which makes it difficult to accurately quantify and correct image degradation factors such as motion blur and poor lighting.
Innovation Solution
A method that uses optically recognized textual characters within an image to calculate a degrading function by comparing them with undistorted elements from a known library, allowing for the deconvolution of this function from the image to enhance its quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional blind image deconvolution is applied on inexpensive digital cameras and mobile devices, then image quality improvement is attempted, but the process fails due to limited computing power and inability to accurately quantify degradation factors
Solution Approach 1:
The patent introduces an intermediary approach by using detected features (text, shapes, patterns) as mediators between the degraded image and the deconvolution process. Instead of directly attempting blind deconvolution which requires immense computing power, the system first detects recognizable features, uses them to infer degradation characteristics, and then applies targeted deconvolution. This intermediary step makes the process feasible on mobile devices with limited computing resources.
Solution Approach 2:
The patent applies preliminary action by performing feature detection and degradation characterization before the actual deconvolution process. The system first identifies text, shapes, or patterns in the degraded image, then uses these detected features to determine the degradation function, and only then proceeds with deconvolution. This preliminary characterization step simplifies the subsequent deconvolution process and reduces computational requirements.
2Measurement precision
If blind image deconvolution is applied in controlled environments with highly calibrated systems, then accurate quantification of degradation factors is achieved, but the method cannot be effectively applied to the wide range of imaging environments encountered by mobile devices
Solution Approach 1:
The patent applies dynamics by making the deconvolution process adaptive to different imaging environments. Instead of relying on fixed calibration data from controlled environments, the system dynamically characterizes degradation by detecting features directly from the captured image. The degradation function is inferred based on the actual image content and detected features, allowing the system to adapt to various lighting conditions, motion states, and optical characteristics encountered in real-world mobile imaging scenarios.
Solution Approach 2:
The system performs self-service by using the degraded image itself to characterize the degradation. Instead of requiring external calibration data or controlled environment measurements, the algorithm detects features within the degraded image (such as text, shapes, or patterns) and uses these self-contained features to infer the degradation function. This self-characterization approach eliminates the need for external calibration systems and enables operation in uncontrolled environments.
3Manufacturing precision
If mathematical deconvolution processes are used to correct image degradation, then image quality is improved, but the process becomes computationally intensive and impractical for real-time processing on mobile platforms
Solution Approach 1:
The patent applies partial action by focusing deconvolution efforts only on the most significant degradation effects identified through feature detection. Instead of attempting full-spectrum deconvolution across the entire image spectrum, the system identifies dominant degradation characteristics from detected features (such as motion blur direction from text distortion) and applies targeted deconvolution parameters. This selective approach maintains image quality improvement while significantly reducing computational complexity and processing time.
Data Source
AI summary
A method for deconvolution of digital images includes obtaining a degraded image from a digital sensor, a processor accepting output from the digital sensor and recognizing a distorted element within the image. The distorted element is compared with a true shape of the element to produce a degrading function. The degrading function is deconvolved from at least a portion of the image to improve image quality of the image. A method of indirectly decoding a barcode includes obtaining an image of a barcode using an optical sensor in a mobile computing device, the image comprising barcode marks and a textual character. The textual character is optically recognized and an image degrading characteristic is identified from the textual character. Compensating for the image degrading characteristic renders previously undecodable barcode marks decodable. A system for deconvolution of digital images is also included.