Image Super-Resolution Using Generative Adversarial Networks with Learned Degradation Operators

Image Super-Resolution Using Generative Adversarial Networks with Learned Degradation Operators

Blog Article

Image super-resolution is a research endeavour that has gained notoriety in computer vision.The research goal is to increase the spatial dimensions of an image using corresponding low-resolution and high-resolution image pairs to enhance the perceptual quality.The challenge of maintaining such perceptual quality lies in developing appropriate algorithms that learn to reconstruct higher-quality images from their lower-resolution counterparts.Recent methods employ deep learning Suspension algorithms to reconstruct textural details prevalent in low-resolution images.

Since corresponding image pairs are non-trivial to collect, researchers attempt super-resolution by creating synthetic low-resolution representations of high-resolution images.Unfortunately, such methods employ ineffective downscaling operations to achieve synthetic low-resolution images.These methods fail to generalize well on real-world images that may suffer different degradations.A different angle is offered to solve the task of image super-resolution by investigating the plausibility of learning the degradational operation using generative adversarial networks.

A two-stage generative adversarial network along with two architectural variations is proposed to solve the task of real-world super-resolution from low-resolution images with unknown degradations.It is demonstrated that learning to downsample images in a weakly supervised manner is an impactful and Home Decor viable approach for super-resolution.