Video Remastering With Joint Degradation Encoding and Restoration
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Solution Overview
Problem
Current deep learning-based video restoration techniques for legacy films, which involve separate denoising and upscaling processes, result in sub-optimal performance and high computational complexity due to the sequential application of independently optimized tools, failing to effectively address multiple types of degradations present in low-resolution, noisy, and scratched videos.
Innovation Solution
A pipelined video processing approach using a degradation encoder that learns interpretable and controllable representations of video degradations through contrastive learning, enabling joint restoration of scratches, noise, and upscaling by employing a backbone network with denoising and super-resolution networks, and allowing for artistic tuning of the restoration process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If separate denoising and upscaling tools are applied sequentially, then specialized restoration functions are achieved, but restoration quality becomes sub-optimal and computational complexity increases
Solution Approach 1:
The patent combines multiple independent restoration tools (denoising, upscaling, scratch removal) into a single unified deep learning model. This model processes degraded video frames through integrated neural network components that simultaneously perform multiple restoration functions, eliminating the need for sequential application of separate tools and reducing computational complexity while improving overall restoration quality.
Solution Approach 2:
The unified restoration model is designed to perform multiple restoration functions simultaneously - denoising, super-resolution upscaling, and scratch removal - within a single multi-functional framework. This universal model accepts degraded video input and produces restored output by jointly optimizing all restoration tasks rather than treating them as separate sequential operations.
2Adaptability or versatility
If independently optimized restoration tools are used in a cascading manner, then specific degradation types are addressed, but overall restoration performance deteriorates
Solution Approach 1:
The patent merges multiple specialized restoration functions into a unified deep learning model that processes all degradation types (noise, blur, scratches, resolution issues) simultaneously. The model integrates denoising, deblurring, scratch removal, and super-resolution capabilities in a single framework that optimizes overall restoration quality rather than sequentially applying independently optimized tools.
Solution Approach 2:
The unified restoration model is structured with segmented functional components - including but not limited to denoising layers, deblurring modules, scratch removal mechanisms, and upscaling networks - that work together in an integrated architecture. Each component addresses specific degradation types while contributing to the overall restoration process within the unified model.
Data Source
AI summary
Restoration methods and systems are disclosed for video remastering. Techniques disclosed include receiving a video sequence. For each frame of the video sequence, techniques disclosed include encoding, by a degradation encoder, a video content associated with the frame into a latent vector. The latent vector is a representation of the degradation present in the video content; the degradation present in the video content includes one or more degradation types. Based on the latent vector and the video content, techniques disclosed further include generating, by a backbone network, one or more feature maps, and, then, restoring the frame based on the one or more feature maps.