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akramkhatami's Projects

bsrn icon bsrn

Blueprint Separable Residual Network for Efficient Image Super-Resolution

edsr-pytorch icon edsr-pytorch

PyTorch version of the paper 'Enhanced Deep Residual Networks for Single Image Super-Resolution' (CVPRW 2017)

esrgan icon esrgan

ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution. The training codes are in BasicSR.

esrganplus icon esrganplus

ICASSP 2020 - ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network - ICPR 2020 - Tarsier: Evolving Noise Injection in Super-Resolution GANs

fmen icon fmen

Lowest memory consumption and second shortest runtime in NTIRE 2022 challenge on Efficient Super-Resolution

imdn icon imdn

Lightweight Image Super-Resolution with Information Multi-distillation Network (ACM MM 2019, Winner Award of ICCVW AIM 2019 Constrained SR Track1&Track2)

involution icon involution

[CVPR 2021] Involution: Inverting the Inherence of Convolution for Visual Recognition, a brand new neural operator

mdf icon mdf

Multi-scale discriminator feature-wise loss function

ntire2017 icon ntire2017

Torch implementation of "Enhanced Deep Residual Networks for Single Image Super-Resolution"

ntire2024_esr icon ntire2024_esr

Solution of the NTIRE 2024 Challenge on Efficient Super-Resolution

rdn icon rdn

Torch code for our CVPR 2018 paper "Residual Dense Network for Image Super-Resolution" (Spotlight)

rfdn icon rfdn

Residual Feature Distillation Network for Lightweight Image Super-Resolution

rlfn icon rlfn

Winner of runtime track in NTIRE 2022 challenge on Efficient Super-Resolution

selfexsr icon selfexsr

Single Image Super-Resolution from Transformed Self-Exemplars (CVPR 2015)

swinir icon swinir

SwinIR: Image Restoration Using Swin Transformer (official repository)

urnet icon urnet

This repository is an official PyTorch implementation of the paper "URNet: An U-shaped Residual Network for Lightweight Image Super-resolution".

wav2pix icon wav2pix

Speech-conditioned face generation using Generative Adversarial Networks

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