Multiscale Video Denoising for Low-Frequency Noise Removal
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Solution Overview
Problem
Typical patch-based denoising algorithms fail to effectively remove low-frequency noise, particularly noticeable in video frames, as they cannot capture large structures with small patches, leading to undesirable viewing experiences.
Innovation Solution
A multiscale video denoising method that decomposes video frames into sub-frames ranging from coarse to fine scales, denoises each scale separately, and reconstructs the final result using a spatiotemporal multiscale approach that integrates information from temporally preceding and subsequent frames, employing techniques like Video Non-local Means (VNLM) for effective noise reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If patch-based denoising algorithms are used, then processing speed is maintained, but low-frequency noise removal capability deteriorates
Solution Approach 1:
The video frame is decomposed into multiple sub-frames at different scales (coarse to fine), allowing the denoising algorithm to process each scale separately. This segmentation enables effective capture of low-frequency noise patterns that span large structures while maintaining processing efficiency through hierarchical processing.
Solution Approach 2:
The invention adds a scale dimension to the denoising process by creating a multi-scale decomposition of video frames. Instead of processing at a single patch scale, the algorithm processes sub-frames at multiple scales (from coarse to fine), effectively adding a dimensional aspect that enables capture of both low-frequency and high-frequency noise patterns.
2Device complexity
If small patches are used for denoising, then computational complexity is reduced, but ability to capture large structures deteriorates
Solution Approach 1:
The denoising process is segmented into multiple scales, where coarse-scale sub-frames capture large structures with larger effective patches, and fine-scale sub-frames handle detailed features. This segmentation allows each processing stage to operate at appropriate complexity levels while collectively covering all structure sizes.
Solution Approach 2:
By introducing multiple scales as an additional dimension, the system can simultaneously analyze both large and small structures. The coarse-scale sub-frames effectively capture large structures that would be missed by small patches, while fine-scale sub-frames maintain the ability to handle detailed features, thus expanding the effective coverage area without proportionally increasing computational complexity.
Data Source
AI summary
Implementations disclosed herein include an image capture device, a system, and a method for performing multiscale denoising of a video. An image processor of the image capture device obtains a video frame. The video frame may be in any format and may include noise artifacts. The image processor decomposes the video frame into one or more sub-frames. In some implementations, the image processor denoises each of the one or more sub-frames. The image processor decomposes one or more video frames in a temporal buffer into one or more temporal sub-frames. The image processor denoises each of the temporal sub-frames. The image processor reconstructs the one or more denoised sub-frames and the one or more temporal sub-frames to produce a denoised video frame. A memory of the image capture device may be configured to store the denoised video frame.