Video Frame Denoising With Recursive Restoration of Compression Noise
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Solution Overview
Problem
Existing video compression methods generate noise that degrades display quality, and current noise removal techniques fail to effectively restore the video to its original quality.
Innovation Solution
An image restoration method using a target denoising network comprising a single-frame network and a recursive network to remove compression noise, leveraging previous frame information to enhance denoising, followed by weighted summation of outputs from both networks to improve display quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video compression is applied to reduce storage space and increase transmission speed, then storage efficiency and transmission speed are improved, but compression noise is generated that degrades display quality
Solution Approach 1:
The patent applies denoising networks to convert the harmful compression noise into beneficial noise reduction. The single-frame network and recursive network process the compressed video frames to remove artifacts while preserving important visual information, effectively transforming the degraded quality caused by compression into improved display quality through intelligent denoising processing
2Object-affected harmful factors
If noise removal techniques are applied to restore video quality, then display quality is improved, but the complexity of the processing system increases
Solution Approach 1:
The patent segments the denoising system into two distinct networks: a single-frame network that processes individual frames for removing compression artifacts, and a recursive network that utilizes temporal information from previous frames. This segmentation allows each network to specialize in specific denoising tasks, improving overall effectiveness while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The patent merges the outputs of the single-frame network and the recursive network through weighted summation. This combination integrates the strengths of both approaches: the single-frame network's ability to remove compression noise and the recursive network's ability to preserve temporal consistency, achieving superior denoising performance without requiring a single overly complex system
3Productivity
If single-frame denoising is applied to remove compression noise, then noise removal efficiency is improved, but temporal consistency between frames is degraded
Solution Approach 1:
The recursive network implements feedback by utilizing the denoised output from previous frames as input for the current frame processing. This feedback mechanism ensures temporal consistency by maintaining continuity across frames, while the single-frame network provides efficient noise removal for each individual frame. The weighted summation combines these results to achieve both efficiency and consistency
Data Source
AI summary
An image restoration method and apparatus, and an electronic device. The method includes: inputting, into a target denoising network, an image to be processed, where the target denoising network includes a single-frame network and a recursive network, and the image to be processed is any frame in a video to be processed (S101); removing, via the single-frame network, compression noise of the image to be processed, and outputting a first image (S102); according to the content of the previous frame of image, removing, via the recursive network, compression noise of the image to be processed, and outputting a second image, where the previous frame of image is the previous frame of image of the image to be processed in the video to be processed (S103); and performing weighted summation on the first image and the second image, and outputting a denoised image for the image to be processed (S104).