Video Noise Reduction via Optical Flow Alignment
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Solution Overview
Problem
Existing video processing technologies face challenges in effectively reducing noise in video sequences, especially in low-quality videos from consumer-grade devices and medical imaging, where noise is prevalent due to suboptimal conditions and camera or object motion, without introducing significant computational overhead or blurring artifacts.
Innovation Solution
The method involves optical flow estimation to align frames, followed by iterative forward and backward remapping using conjugate gradient minimization or bilateral filtering in time and space, which reduces noise while maintaining the original resolution and avoiding additional blurring, leveraging redundancy in video sequences and using robust statistical methods like Bayesian reconstruction and bilateral filtering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If noise reduction filtering is applied to video sequences, then noise is reduced, but temporal and spatial artifacts are introduced
Solution Approach 1:
The patent performs preliminary alignment of video frames using optical flow estimation and feature matching before noise reduction. By pre-aligning frames to a reference frame, the method ensures that corresponding pixels across frames represent the same spatial location, preventing misalignment artifacts during the noise reduction filtering process
Solution Approach 2:
The patent segments the video processing into distinct stages: optical flow estimation, feature matching, frame alignment, and noise reduction. This segmentation allows each stage to be optimized independently, with the alignment stage preparing clean input for the noise reduction stage, thereby preventing artifact propagation
2Object-affected harmful factors
If multiple frames are processed for noise reduction, then noise reduction effectiveness is improved, but computational cost increases
Solution Approach 1:
The patent processes only a selected number of frames (e.g., 3-5 frames) rather than all frames in a video sequence. By choosing an optimal subset that provides sufficient noise reduction without processing every frame, the method achieves effective noise reduction while limiting computational cost to manageable levels
Solution Approach 2:
The patent performs preliminary frame selection and alignment before the computationally intensive noise reduction step. By pre-processing frames to identify and align the most useful frames, the method reduces the computational burden of the subsequent noise reduction operation while maintaining effectiveness
3Measurement precision
If video frames are aligned using optical flow, then alignment accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments optical flow computation into coarse and fine stages. The coarse stage uses a simplified optical flow algorithm for rapid initial alignment, while the fine stage applies more accurate but computationally intensive methods only to residual misalignments. This segmentation achieves high alignment accuracy while minimizing overall processing time
Solution Approach 2:
The patent applies full-precision optical flow estimation only to key reference frames and uses lighter-weight alignment methods for intermediate frames. This partial application of computationally intensive alignment techniques maintains accuracy where needed while reducing processing time for the overall sequence
Data Source
AI summary
In some embodiments, a method of processing a video sequence may include receiving an input video sequence having an input video sequence resolution, aligning images from the input video sequence, reducing noise in the aligned images, and producing an output video sequence from the reduced noise images, wherein the output video sequence has the same resolution as the input video sequence resolution. Other embodiments are disclosed and claimed.