Image Registration Memory Retrieval Using K-Means Stripe Fetching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional image registration techniques suffer from high memory bandwidth overhead and inefficiency in retrieving all required pixels, particularly when aligning consecutive frames in video sequences.
Innovation Solution
The method involves splitting the image into multiple vertical stripes and using a simplified K-means algorithm to track two clusters of source-pixels, allowing for efficient memory access and retrieval of a larger percentage of pixels with minimal overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional random-access techniques are used to read small two-dimensional blocks of image data into internal cache, then image warping can be performed, but memory bandwidth overhead is high and not all required pixels can be retrieved
Solution Approach 1:
The patent divides the reference image into multiple vertical stripes or columns, processing each stripe independently. This segmentation allows the system to retrieve pixels in a more systematic pattern rather than random access, improving retrieval completeness while reducing redundant memory bandwidth usage. Each stripe is processed through dedicated logic units that track and retrieve required source pixels more efficiently.
Solution Approach 2:
The patent implements dynamic tracking of pixel clusters using simplified K-means algorithm within the image registration logic. This dynamic adaptation allows the system to identify and retrieve only the necessary pixel regions adaptively, improving retrieval completeness while minimizing memory bandwidth overhead by avoiding static, inefficient access patterns.
2Productivity
If image data is fetched using traditional techniques, then processing can be performed, but the retrieval rate of required pixels is insufficient
Solution Approach 1:
The patent performs preliminary organization of reference image data into vertical stripes and pre-identifies pixel clusters using K-means algorithm before actual warping operations. This preliminary action enables more efficient retrieval during processing, increasing the retrieval rate of required pixels while reducing unnecessary memory bandwidth consumption during the main processing phase.
Solution Approach 2:
The patent changes the memory access pattern parameter from traditional random block access to systematic vertical stripe traversal with cluster-based selection. This parameter change in the access pattern improves the retrieval rate by ensuring systematic coverage of required pixels while reducing memory bandwidth overhead through optimized access sequences.
3Ease of operation
If conventional image registration methods are used, then alignment can be achieved, but memory access efficiency is poor
Solution Approach 1:
The patent segments the image registration process into independent vertical stripe processing units, each with dedicated logic for pixel cluster identification and retrieval. This segmentation improves memory access efficiency by creating localized, manageable processing units while the overall system complexity is managed through the systematic and repetitive nature of the stripe-based approach.
Solution Approach 2:
The patent replaces traditional complex random-access memory control mechanisms with a simplified systematic traversal approach based on vertical stripes and cluster identification. This substitution improves memory access efficiency by using simpler, more predictable access patterns while maintaining registration accuracy through the K-means based pixel cluster tracking.
Data Source
Figure 1
Figure 2A
Figure 2B
AI summary
Systems and methods are provided for image registration, which is used in video noise reduction and other multi-frame processing applications, such as remote sensing, medical imaging, and computer vision. Image registration can include geometrically transforming a current image to align with a reference image, ensuring that objects, patterns, and/or features in the images appear in the same spatial location. Memory is accessed for image retrieval for mapping pixels in the main image to corresponding pixels in a reference image. To minimize overhead in memory-bandwidth and achieve a high retrieval rate, the two-dimensional plane is split into vertical stripes, and, for each vertical stripe, an independent one-dimensional fetch-mechanism is formulated based on tracking two clusters of source-pixels using a simplified K-means algorithm. Using two clusters of source pixels enables fetching pixels from across discontinuities in the motion-field The systems and methods allow for power-efficient multi-frame processing and high-quality video noise reduction.