Alternatives and similar repositories for neural-preset-pytorch:

Users that are interested in neural-preset-pytorch are comparing it to the libraries listed below

• birdortyedi / DeNIM
  Official Repository for Deterministic Neural Illuminant Mapping (DeNIM) published in ICCV2023 Workshops
  ☆15Updated last year
• AndreGuo / ITMLUT
  Official PyTorch implementation of "Redistributing the Precision and Content in 3D-LUT-based Inverse Tone-mapping for HDR/WCG Display" in…
  ☆41Updated 8 months ago
• ImCharlesY / SepLUT
  [ECCV 2022] Official PyTorch Implementation of "SepLUT: Separable Image-adaptive Lookup Tables for Real-time Image Enhancement" (https://…
  ☆48Updated 2 years ago
• zmzhang1998 / Real-RawVSR
  Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark Dataset (Real-RawVSR), ECCV 2022
  ☆55Updated last year
• SSRheart / TSFlow
  ☆12Updated 8 months ago
• cao-cong / UnCLTMO
  Unsupervised HDR Image and Video Tone Mapping via Contrastive Learning. TCSVT 2023
  ☆15Updated 6 months ago
• mv-lab / nilut
  [AAAI 2024] NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement. Project Website https://mv-lab.github.io/nilut/
  ☆71Updated 11 months ago
• cszhilu1998 / RAW-to-sRGB
  [ICCV 2021] Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision
  ☆86Updated 2 years ago
• yinzhicun / MetaF2N
  MetaF2N: Blind Image Super-Resolution by Learning Efficient Model Adaptation from Faces (ICCV 2023)
  ☆56Updated last year
• sunny2109 / MobileSR-NTIRE2022
  [NTIRE 2022, EfficientSR] MobileSR: A Mobile-friendly Transformer for Efficient Image Super-Resolution
  ☆71Updated last year
• ChengxuLiu / 4DLUT
  [TIP'23] 4D LUT: Learnable Context-Aware 4D Lookup Table for Image Enhancement
  ☆15Updated last year
• nagejacob / RecurrentMobileNet
  Light parameters raw image denoising, 2nd place in MegCup 2022
  ☆30Updated 2 years ago
• jiangyitong / RCD
  ☆29Updated last year
• lin-zhao-resoLve / Deep-Symmetric-Network-Enhancement
  ☆18Updated last year
• XPixelGroup / SEAL
  ICLR 2024 (Spotlight) - SEAL: A Framework for Systematic Evaluation of Real-World Super-Resolution
  ☆50Updated last year
• HUST-IAL / CoTF
  The code of paper "Real-Time Exposure Correction via Collaborative Transformations and Adaptive Sampling" (CVPR 2024)
  ☆19Updated 10 months ago
• yamand16 / ExposureCorrection
  ☆58Updated 7 months ago
• TencentARC / FastRealVSR
  Codes for "Mitigating Artifacts in Real-World Video Super-Resolution Models"
  ☆59Updated 2 years ago
• NeonLeexiang / DLGSANet
  ☆89Updated last year
• HaomingCai / CUGAN
  [ Official ] - Toward Interactive Modulation for Photo-Realistic Image Restoration. CVPRW 2021 NTIRE.
  ☆23Updated 2 years ago
• dasongli1 / Learning_degradation
  ☆58Updated last year
• liuguandu / RC-LUT
  ☆30Updated last year
• ImCharlesY / AdaInt
  [CVPR 2022] Official PyTorch Implementation of "AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement" …
  ☆178Updated 2 years ago
• csxmli2016 / ReDegNet
  From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution (ECCV 2022)
  ☆115Updated 2 years ago
• Algolzw / NCNet
  [AIM & ECCVW 2022] Fast Nearest Convolution for Real-Time Image Super-Resolution
  ☆70Updated last year
• zfu006 / sRGB-Real-Noise-Synthesis
  Official PyTorch implementation of "sRGB Real Noise Synthesizing with Neighboring Correlation-Aware Noise Model" in CVPR 2023.
  ☆30Updated last year
• Xian-Bei / CLUT
  Learning Compressed Representation of 3DLUT for Image-enhancement. Higher performance with much smaller models!
  ☆104Updated last year
• gosha20777 / rawformer
  Unpaired Raw-to-Raw Translation for Learnable Camera ISPs
  ☆26Updated last week
• megvii-research / D2C-SR
  Official MegEngine implementation of ECCV2022 "D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-Resolution".
  ☆45Updated 2 years ago
• csjliang / DASR
  Official implementation of the paper 'Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution' in ECCV 2022
  ☆139Updated 2 years ago