Image Deblurring via Motion-Based Segmentation
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Solution Overview
Problem
Existing image deblurring techniques fail to effectively distinguish between camera motion and object motion, leading to disappointing results when dealing with blurriness caused by multiple unrelated factors in digital photography.
Innovation Solution
A computer-implemented method that captures a sequence of images, generates image segments based on motion estimates, and performs deblurring operations to reduce blurriness caused by combined effects of moving objects and camera motion, enhancing image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single deblurring operation is applied to the entire image assuming uniform blur, then the processing is simple and fast, but the deblurring results are disappointing when multiple motion types are present
Solution Approach 1:
The image is divided into multiple image segments based on motion estimates from a sequence of images. Each segment corresponds to regions with different motion characteristics (camera motion vs. object motion). This segmentation allows different deblurring operations to be applied to different segments, resolving the contradiction between processing simplicity and deblurring accuracy.
Solution Approach 2:
Different deblurring operations are applied to different image segments based on their specific motion characteristics. Rather than applying a uniform deblurring operation to the entire image, the system tailors the deblurring approach to local regions, improving overall deblurring accuracy while maintaining reasonable processing complexity.
2Device complexity
If the deblurring method assumes the whole image is blurred the same way, then the processing is straightforward, but it cannot distinguish between camera motion and object motion
Solution Approach 1:
The image is segmented into regions with different motion characteristics by analyzing a sequence of captured images. Motion estimates are generated for each image segment, allowing the system to distinguish between camera-induced motion and object-induced motion. This segmentation approach enables accurate motion type discrimination without requiring overly complex analysis of the entire image uniformly.
Solution Approach 2:
Motion estimates are generated in advance for each image segment before the actual deblurring operation. By pre-analyzing the motion characteristics of different segments and identifying their motion types, the system prepares the necessary information for targeted deblurring, reducing the complexity of the main processing step while maintaining high discrimination accuracy.
3Manufacturing precision
If multiple image segments are processed with different deblurring operations, then the deblurring accuracy improves, but the processing complexity increases
Solution Approach 1:
The image is divided into segments based on motion characteristics, and each segment is processed with an appropriate deblurring operation. This segmentation strategy improves deblurring accuracy by treating different motion types differently, while the modular nature of segment-based processing keeps the overall system complexity manageable through reuse of common processing components.
Solution Approach 2:
Motion estimates and segment classifications are computed in advance before the actual deblurring operations. This preliminary analysis organizes the work in a way that reduces the complexity of the main processing step, as the segmentation and motion type identification are performed once and then reused across multiple processing operations.
Data Source
AI summary
An image capture application captures a sequence of images via a digital camera. The sequence of images may have undesirable levels of blurriness due to the motion of objects in the field of view of the digital camera or due to movement of the digital camera itself. A deblur engine within the image capture application generates image segments within one of the captured images, where a given image segment includes pixel values that move coherently between different images in the sequence. The deblur engine then deblurs each image segment based on the coherent motion of each different image segment and combines the resultant, deblurred image segments into a deblurred image. Advantageously, blurriness caused by the combined effects of moving objects and camera motion may be reduced, thereby improving the ability of a digital camera to provide high-quality images. As such, the user experience of digital photography may be enhanced.