This paper proposes a real-time FPGA-based architecture of improved ORB. It proposes a strategy of redistribution of ORB feature points, which solves the problem of sorting FAST points of the whole image by response score. Besides, a strategy for offline generation of rBrief point pair patterns is proposed, which avoids online rotation of neighborhood pixels of feature points. These two strategies greatly reduce the resource consumption and processing clock cycles of the whole architecture. What’s more, the data throughput of the feature extraction step and feature description step is maximized, and finally a completely pipeline architecture is obtained. Due to the tips for parallel processing and resource reuse, the hardware implementation of the proposed architecture costs very few resources and processing cycles. The experimental results show that this architecture can detect feature and extract descriptor from video streams of 1280x720 resolution at 161 frames per second (161 fps), and the extracted ORB features perform well.
Image matching is a fundamental task in computer vision. It is used to establish correspondence between two images
taken at different viewpoint or different time from the same scene. However, its large computational complexity has
been a challenge to most embedded systems. This paper proposes a single FPGA-based image matching system, which
consists of SIFT feature detection, BRIEF descriptor extraction and BRIEF matching. It optimizes the FPGA architecture
for the SIFT feature detection to reduce the FPGA resources utilization. Moreover, we implement BRIEF description and
matching on FPGA also. The proposed system can implement image matching at 30fps (frame per second) for 1280x720
images. Its processing speed can meet the demand of most real-life computer vision applications.
Blind motion deblurring from a single image is a challenging ill-posed problem. Significant progress has been made
since blur kernel estimation method using salient edge prediction on transfer region is proposed. However, as selection
rule for points to estimate the blur kernel has not been researched deeply, some texture and noise points were taken into
account for blur kernel estimating, which makes the existing methods not robust enough. This paper propose a robust
motion deblurring algorithm using salient edge prediction on transfer region, which employs a new metric to select
transfer region points for kernel estimation. A novel kernel refinement method with hysteresis thresholding is also
proposed and adopted by the algorithm to reduce the kernel noise. Extensive experiments show that the algorithm
achieves good results, while both the new metric and the novel kernel refinement method improve robustness of the
restoration algorithm.
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