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

Open mihirparadkar opened 2 years ago

mihirparadkar commented 2 years ago

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

AnimeshMaheshwari22 commented 1 year ago

Hi. Are we taking this task? Would like to contribute.

github-actions[bot] commented 9 months ago

This issue is stale because it has been open for 180 days with no activity. It will be closed if no further activity occurs. Thank you.