Abstract:
Deep convolutional neural network-based image super-resolution (SR) models are better at recovering high-resolution (HR) images from predefined downscaling methods.
This image processing projects proposes a content adaptive resampler (CAR)-based learned image downscaling method that considers upscaling. The proposed resampler network generates content adaptive image resampling kernels to generate pixels on the downscaled image from the HR input. Read also Python Projects.
A differentiable upscaling (SR) module upscales the LR result to HR. The proposed framework achieves state-of-the-art SR performance through upscaling guided image resamplers that adaptively preserve detailed information needed for upscaling by back-propagating the reconstruction error down to the original HR input across the entire framework to adjust model parameters.
Experimental results show that deep SR models trained jointly with the CAR model improve SR performance and generate LR images comparable to interpolation-based methods. Read also Machine Learning Projects.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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