Invertible Rescaling Network for Adjustable Image Super-Resolution
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Solution Overview
Problem
Conventional image super-resolution models face challenges in achieving a balance between objective quality and perceptual quality, often resulting in blurred outputs with low accuracy or sharp outputs with lower accuracy, due to the conflict between mean squared error and adversarial losses, and are inefficient as they require separate models for objective and perceptual quality optimization.
Innovation Solution
The implementation of an invertible rescaling neural network (IRNN) with joint optimization using a mixture of reconstruction, perceptual, and adversarial losses, conditioned on a randomly sampled auxiliary latent variable, allows for an adjustable trade-off between distortion and perception during inference by modulating the latent feature sampling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional SR models optimize for objective quality (distortion), then accuracy is improved, but perceptual quality (edge sharpness, feature resolvability) deteriorates
Solution Approach 1:
The patent implements a dynamic trade-off mechanism where the model can adjust between objective and perceptual quality optimization based on a controllable parameter. The system transitions from static optimization to dynamic adaptation, allowing users to select the desired balance between accuracy and perceptual quality for different application scenarios.
Solution Approach 2:
The patent introduces a trade-off parameter that controls the weighting between distortion loss and perceptual loss during optimization. By changing this parameter, the system can dynamically adjust the optimization focus between objective accuracy and perceptual quality, resolving the contradiction through parameter-based control.
2Manufacturing precision
If conventional SR models optimize for perceptual quality, then edge sharpness and feature resolvability are improved, but accuracy deteriorates
Solution Approach 1:
The model enables dynamic switching between perceptual quality optimization and accuracy optimization through a controllable trade-off parameter. When perceptual quality is prioritized, the system adjusts the loss function weighting to favor perceptual metrics while maintaining the capability to revert to accuracy-focused optimization when needed.
Solution Approach 2:
By modifying the trade-off parameter in the optimization process, the system can shift the balance between perceptual quality and accuracy. This parameter change mechanism allows flexible adjustment of optimization priorities without requiring separate models for each objective.
3Reliability
If separate models are used for objective and perceptual quality optimization, then both qualities can be optimized independently, but system complexity and computational efficiency deteriorate
Solution Approach 1:
The patent merges the previously separate objective quality optimization model and perceptual quality optimization model into a single unified model. This unified model incorporates both distortion loss and perceptual loss in its objective function, allowing simultaneous optimization of both qualities within one framework, thereby reducing system complexity while maintaining optimization effectiveness.
Solution Approach 2:
The unified model serves multiple functions: it can optimize for objective quality, perceptual quality, or any combination thereof, depending on the trade-off parameter setting. This multi-functionality replaces the need for separate specialized models, simplifying the system architecture while preserving the ability to optimize for different quality aspects.
4Device complexity
If a single unified model is used for both objective and perceptual quality optimization, then model complexity is reduced, but the ability to independently optimize each quality deteriorates
Solution Approach 1:
The unified model incorporates a trade-off parameter that enables independent optimization control. By adjusting this parameter, users can shift the optimization focus between objective quality and perceptual quality, effectively maintaining independent optimization capability within the unified framework. The parameter acts as a control mechanism that preserves optimization independence while benefiting from model unification.
Solution Approach 2:
The unified model implements dynamic optimization control where the relative importance of objective and perceptual quality can be adjusted during the optimization process. This dynamic control mechanism ensures that the unified model can adaptively prioritize different quality aspects as needed, maintaining optimization effectiveness despite the unified architecture.
Data Source
AI summary
Presented herein are embodiments of systems and methods for training a system and for using a trained system to generate super-resolution imagery from low-resolution imagery. Embodiments for generating super-resolution imagery from low-resolution imagery include obtaining an input trade-off parameter that indicates a preference regarding low distortion or high perceptual quality for a generated SR image and obtaining a latent variable from a distribution defined, at least in part, by a trade-off parameter. Embodiments include inputting an LR image and the latent variable into an embodiment of an invertible rescaling network (IRNN) in an inverse upscaling direction of the IRNN to generate an output SR image that comprises accuracy and perception qualities conditioned by the input trade-off parameter. In one or more embodiments, a trained IRNN uses a trade-off parameter that indicates a desired trade-off between whether the trained IRNN generates an SR image having lower distortion or higher perceptual quality.