Semantic Super-Resolution GAN for Aerial Imagery
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Solution Overview
Problem
Conventional systems for upscaling aerial imagery to fill missing or blurry sections often result in images that are not semantically faithful, reducing their accuracy and requiring complex, computationally intensive learning algorithms.
Innovation Solution
An image processing system that uses a trained residual-in-residual dense network (RRDN) and a generative adversarial network (GAN) discriminator to generate up-sampled images based on input images, image realism prediction values, and a semantic feature mask, ensuring semantic fidelity and resolution consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional upscaling systems are used to fill missing or blurry sections, then the resolution is improved, but the semantic fidelity deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the upscaled image is fed back into the network along with the original low-resolution image. The network compares the upscaled output with the feedback input and iteratively refines the semantic features to ensure fidelity to the original image content while achieving high resolution. This is achieved through the recursive application of the upsampling function where f(x, f(x)) is computed, allowing the system to learn from its own outputs and correct semantic drift.
Solution Approach 2:
The patent employs a nested structure where the upsampling function contains multiple levels of processing. The outer function performs the primary upsampling while the inner function recursively processes the output, creating a nested computational structure. This nested arrangement allows the system to maintain semantic features at multiple scales simultaneously, ensuring that fine details are preserved while achieving overall high resolution.
2Manufacturing precision
If complex learning algorithms are used to upsample imagery, then the resolution is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the upsampling task into distinct functional components: a feature extraction module that identifies semantic features, an upsampling module that increases resolution, and a refinement module that ensures semantic fidelity. By dividing the complex task into these manageable segments, the system achieves high resolution upsampling with reduced computational complexity compared to monolithic approaches. Each segment can be optimized independently and processed in a pipelined manner.
Data Source
AI summary
An image processing system includes circuitry that generates up-sampled images corresponding to input images based on the input images, image realism prediction values, and a corresponding semantic feature mask. The circuitry generates the image realism prediction values based on at least one of the input images and the up-sampled images, and generates the semantic feature mask based on at least one of the input images and the up-sampled images.