Mobile Document Image Artifact Correction via Cloud Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Camera-captured document images often contain artifacts such as clutter, poor resolution, non-uniform illumination, and distortion, requiring significant computation resources for correction, which is challenging due to limited processing capabilities of handheld devices.
Innovation Solution
A hybrid approach where a mobile device captures an image and determines the region-of-interest, with metadata indicating user refinement, and transmits this information to the cloud for processing, leveraging cloud computing resources for artifact removal while providing real-time feedback and preview processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If artifact correction is performed using traditional methods on camera-captured images, then image quality improves, but computation resources and processing time increase significantly
Solution Approach 1:
The system segments the image processing task by identifying and isolating the region-of-interest (ROI) from the rest of the image. Only the ROI undergoes intensive artifact correction processing, while the remaining image areas use standard processing. This segmentation allows high-quality correction to be applied selectively, reducing overall computation resources while maintaining image quality in the important region.
Solution Approach 2:
The system applies different processing qualities to different regions of the image. The ROI receives intensive artifact correction with high manufacturing precision, while other regions receive standard processing. This local quality approach ensures that computation resources are concentrated where they are most needed, improving image quality in critical areas without uniformly increasing processing complexity across the entire image.
2Manufacturing precision
If full-resolution images are processed for artifact removal, then correction effectiveness improves, but processing time and computational load increase
Solution Approach 1:
The system processes only the segmented ROI at full resolution for intensive artifact correction, rather than processing the entire full-resolution image. This segmentation strategy maintains correction effectiveness within the ROI while significantly reducing the total processing time and computational load by excluding non-ROI areas from intensive processing.
Solution Approach 2:
The system applies partial action by performing intensive artifact correction only on the necessary ROI portion of the image rather than the entire image. This partial processing approach achieves sufficient correction effectiveness for the important region without the excessive time and computational resources that would be required to process the complete full-resolution image.
3Device complexity
If cloud processing is used for artifact removal, then computation resources are sufficient, but transmission time and network dependency increase
Solution Approach 1:
The system performs preliminary actions by conducting initial image processing and ROI identification on the mobile device before cloud transmission. This preliminary processing reduces the amount of data that needs to be transmitted to the cloud and prepares the image in advance, thereby reducing transmission time and making more efficient use of cloud computation resources when the image is eventually processed.
4Manufacturing precision
If user refinement of region-of-interest is enabled, then processing accuracy improves, but user interaction time increases
Solution Approach 1:
The system implements feedback by providing users with visual display of the automatically detected ROI and allowing them to review and refine it if needed. This feedback mechanism enables users to improve processing accuracy by adjusting the ROI boundaries based on what they see, while the initial automatic detection reduces the overall interaction time by providing a good starting point that often requires minimal or no refinement.
Data Source
AI summary
Aspects of the present invention are related to systems and methods for correcting artifacts in a camera-captured image of a document or image of an object exhibiting document-like content. A mobile device may capture an image and send the image to a cloud computing system for processing. According to a first aspect of the present invention, the mobile device may provide real-time feedback cues to assist in the capture of an image. The mobile device may detect a region-of-interest in the captured image, and a user may refine or confirm the detected region-of-interest. The captured image, information identifying the region-of-interest and a metadata tag, referred to as a region-of-interest modification tag, indicating whether, or not, the region-of-interest was refined by a user may be sent to the cloud. The cloud may process the image giving priority to the region-of-interest received from the handset when the region-of-interest modification tag indicates that the region-of-interest was refined by a user over a cloud determined region-of-interest. The cloud may transmit, to the handset, the processing results.