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Layers, loss functions, datasets, and models for Single Image Super-Resolution (SISR) #448

@mihirparadkar

Description

@mihirparadkar

Short Description
Single-Image Super-Resolution describes the domain of enhancing image resolution for single images (as opposed to groups of images of a scene, for example). Solutions in this domain have applications in security, medical imaging, segmentation, and video enhancement.

Papers
Survey paper (2021): From Beginner to Master: A Survey for Deep
Learning-based Single-Image Super-Resolution

Datasets:
Berkeley Segmentation Data Set and Benchmarks 500 (BSDS500)

NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study
Div2K Dataset

Layers
Sub-pixel convolution: Real-Time Single Image and Video Super-Resolution Using an Efficient
Sub-Pixel Convolutional Neural Network

Loss Functions
Gradient Prior Loss
Edge Prior Loss

Metrics
Peak Signal-to-Noise Ratio (PSNR)
Structural Similarity index measure (SSIM)

Existing Implementations
https://keras.io/examples/vision/super_resolution_sub_pixel/
https://github.com/krasserm/super-resolution

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