Papers with Code 아카이브
Single-Image-Based Hdr Reconstruction
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/JihyongOh/JSI-GAN
공식 코드https://github.com/NJUVISION/CBUnet
공식 코드Rendering Nighttime Image Via Cascaded Color and Brightness Compensation
https://github.com/aamir-mustafa/distil_style
공식 코드Distilling Style from Image Pairs for Global Forward and Inverse Tone Mapping
https://github.com/alex04072000/SingleHDR
공식 코드Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline
https://github.com/andreguo/itmlut
공식 코드Redistributing the Precision and Content in 3D-LUT-based Inverse Tone-mapping for HDR/WCG Display
https://github.com/bcjuan/itmmqnet
공식 코드A Mixed Quantization Network for Computationally Efficient Mobile Inverse Tone Mapping
https://github.com/cao-cong/uncltmo
공식 코드Unsupervised HDR Image and Video Tone Mapping via Contrastive Learning
https://github.com/compphoto/IntrinsicHDR
공식 코드https://github.com/cs-gangxu/fdan
공식 코드https://github.com/donkeymouse/thermalchameleon
공식 코드Thermal Chameleon: Task-Adaptive Tone-mapping for Radiometric Thermal-Infrared images
https://github.com/fengzhang427/LLF-LUT
공식 코드Lookup Table meets Local Laplacian Filter: Pyramid Reconstruction Network for Tone Mapping
https://github.com/frozenburning/text2light
공식 코드https://github.com/hulitaotom/Joint-Multi-Scale-Tone-Mapping-and-Denoising-for-HDR-Image-Enhancement
공식 코드Joint Multi-Scale Tone Mapping and Denoising for HDR Image Enhancement
https://github.com/j-fhu/udcunet
공식 코드UDC-UNet: Under-Display Camera Image Restoration via U-Shape Dynamic Network
https://github.com/jieyang1987/Tone-Mapping-Based-on-Multi-scale-Histogram-Synthesis
공식 코드https://github.com/jqtangust/epce-hdr
공식 코드High Dynamic Range Image Reconstruction via Deep Explicit Polynomial Curve Estimation
https://github.com/linmc86/Deep-Reformulated-Laplacian-Tone-Mapping
공식 코드https://github.com/lwt3437/lanet
공식 코드Luminance Attentive Networks for HDR Image and Panorama Reconstruction
https://github.com/mdyao/csnorm
공식 코드Generalized Lightness Adaptation with Channel Selective Normalization
https://github.com/mukulkhanna/fhdr
공식 코드FHDR: HDR Image Reconstruction from a Single LDR Image using Feedback Network
https://github.com/mv-lab/AISP
공식 코드Simple Image Signal Processing using Global Context Guidance
https://github.com/mv-lab/nilut
공식 코드NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement
https://github.com/postech-ami/hdr-plenoxels
공식 코드HDR-Plenoxels: Self-Calibrating High Dynamic Range Radiance Fields
https://github.com/princeton-computational-imaging/DirtyPixels
공식 코드Dirty Pixels: Towards End-to-End Image Processing and Perception
https://github.com/sergiuoprea/hgan
공식 코드https://github.com/sharif-apu/twostageHDR_NTIRE21
공식 코드A Two-stage Deep Network for High Dynamic Range Image Reconstruction
https://github.com/shreyesss/hdrsplat
공식 코드HDRSplat: Gaussian Splatting for High Dynamic Range 3D Scene Reconstruction from Raw Images
https://github.com/smbu-mm/llie
공식 코드Towards Realistic Low-Light Image Enhancement via ISP Driven Data Modeling
https://github.com/sooyekim/Deep-SR-ITM
공식 코드Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR Applications
https://github.com/srameo/le3d
공식 코드Lighting Every Darkness with 3DGS: Fast Training and Real-Time Rendering for HDR View Synthesis
https://github.com/tfwang-9527/gs-3i
공식 코드GS-I$^{3}$: Gaussian Splatting for Surface Reconstruction from Illumination-Inconsistent Images
https://github.com/thisisvikki/itm-baseline
공식 코드Lightweight Improved Residual Network for Efficient Inverse Tone Mapping
https://github.com/vinairesearch/single_image_hdr
공식 코드Single-Image HDR Reconstruction by Multi-Exposure Generation
https://github.com/yuyanzhou1/improving-lens-flare-removal
공식 코드Improving Lens Flare Removal with General Purpose Pipeline and Multiple Light Sources Recovery
https://github.com/MindSpore-scientific/code-13/tree/main/Luminance-Guided-Chrominance-Enhancement-for-HEVC-Intra-Coding
Luminance Attentive Networks for HDR Image and Panorama Reconstruction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.