Delta Frame Denoising for Misaligned Image Frames
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Solution Overview
Problem
Image capture devices, particularly wearable devices, face challenges in denoising misaligned frames with high noise due to movement, leading to increased processing cycles, resource consumption, and power usage, especially when identifying matching frames is difficult.
Innovation Solution
The method involves generating a delta frame by subtracting a reference frame from an image frame, performing denoising on the delta frame, and then adding the denoised delta frame back to the reference frame to produce an output image frame, thereby reducing temporal noise without the need for frame alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional denoising techniques are used on misaligned frames, then image quality may be improved, but processing complexity and power consumption increase significantly
Solution Approach 1:
The patent segments the denoising process into two distinct stages: (1) alignment operation to generate a aligned frame from the current frame and reference frame, and (2) denoising operation applied only to the delta frame (difference between current and aligned frames). This segmentation allows the computationally intensive denoising to be applied only to the essential motion components rather than entire frames, significantly reducing power consumption while maintaining denoising effectiveness.
Solution Approach 2:
The patent performs alignment as a preliminary action before denoising. By first aligning the reference frame with the current frame and computing the delta frame, the system prepares the data in a form that enables efficient denoising. This preliminary alignment step ensures that subsequent denoising operations work on properly registered data, improving both effectiveness and efficiency.
2Object-affected harmful factors
If matching frames are identified for denoising, then denoising effectiveness improves, but processing time and computational resources increase
Solution Approach 1:
Instead of the conventional approach of finding matching frames and then denoising them, the patent inverts the process: it computes the delta frame (difference) first, then applies denoising to the delta frame, and finally combines it with the reference frame. This inversion eliminates the need for complex frame matching algorithms, significantly reducing processing time while maintaining denoising quality.
Solution Approach 2:
The patent extracts only the essential motion information by computing the delta frame (difference between current and aligned frames) and applies denoising only to this extracted component. This extraction approach avoids processing redundant information present in static or slowly varying regions of the frames, reducing computational time while preserving important motion details.
3Measurement precision
If frame alignment is performed before denoising, then denoising accuracy improves, but device complexity increases
Solution Approach 1:
The patent segments the complex alignment and denoising task into modular operations: alignment computation, delta frame computation, denoising of delta frame, and final combination. Each segment is independently optimized and can be implemented with specialized hardware or software routines, making the overall complex process more manageable and implementable in resource-constrained devices.
4Object-affected harmful factors
If denoising is applied to all frames, then image quality improves, but processing resources are consumed excessively
Solution Approach 1:
The patent extracts only the necessary motion components (delta frames) for denoising processing, rather than processing entire frames. This extraction significantly reduces the data volume requiring denoising computation, thereby increasing the number of frames that can be processed per second while still achieving effective noise reduction in the final output frames.
Data Source
AI summary
In some aspects, an apparatus includes a processing system including one or more memories and one or more processors coupled to the one or more memories. The processing system is configured to obtain an image frame, to obtain a first delta frame corresponding to a difference between the image frame and a reference frame, and to perform a denoising operation associated with the first delta frame to generate a second delta frame. The processing system is further configured to obtain an output image frame corresponding to a sum of the second delta frame and the reference frame and to perform one or more operations using the output image frame.