https://github.com/gojasper/lbm
공식 코드LBM: Latent Bridge Matching for Fast Image-to-Image Translation
Papers with Code 아카이브
Image Enhancement
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 10
- 평가표
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- 보존된 평가 기록
- 26
Image Enhancement is basically improving the interpretability or perception of information in images for human viewers and providing ‘better’ input for other automated image processing techniques. The principal objective of Image Enhancement is to modify attributes of an image to make it more suitable for a given task and a specific observer.
Source: A Comprehensive Review of Image Enhancement Techniques
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/google-research/maxim
공식 코드https://github.com/google/hdrnet
공식 코드https://github.com/google/nerfactor
공식 코드NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illumination
https://github.com/googolplexgoodenough/noise_estimate
공식 코드https://github.com/groupg-yk/mtur-net
공식 코드Medium Transmission Map Matters for Learning to Restore Real-World Underwater Images
https://github.com/guanguanboy/Semi-LLIE
공식 코드Semi-LLIE: Semi-supervised Contrastive Learning with Mamba-based Low-light Image Enhancement
https://github.com/guanguanboy/hsie
공식 코드https://github.com/gusanagy/udbe
공식 코드UDBE: Unsupervised Diffusion-based Brightness Enhancement in Underwater Images
https://github.com/gy65896/onerestore
공식 코드OneRestore: A Universal Restoration Framework for Composite Degradation
https://github.com/halmstaduniversitybiometrics/lfw-beautified
공식 코드LFW-Beautified: A Dataset of Face Images with Beautification and Augmented Reality Filters
https://github.com/hangxiaotian/perceptual-multi-exposure-image-fusion
공식 코드https://github.com/hanxuhfut/decoupled-low-light-image-enhancement
공식 코드https://github.com/healer-ml/msfnet-cpd
공식 코드MSFNet-CPD: Multi-Scale Cross-Modal Fusion Network for Crop Pest Detection
https://github.com/heasonlee/sfin
공식 코드Noise Calibration and Spatial-Frequency Interactive Network for STEM Image Enhancement
https://github.com/heeheee888/fd3
공식 코드https://github.com/hejh8/cfwd
공식 코드Low-light Image Enhancement via CLIP-Fourier Guided Wavelet Diffusion
https://github.com/hejh8/cyclerdm
공식 코드https://github.com/hejh8/joint-wavelet-and-fourier-priors-guided-diffusion
공식 코드Zero-Shot Low-Light Image Enhancement via Joint Frequency Domain Priors Guided Diffusion
https://github.com/hengliusky/UltraSound_SSSR
공식 코드Perception Consistency Ultrasound Image Super-resolution via Self-supervised CycleGAN
https://github.com/heverlaw/pcenet-image-enhancement
공식 코드Degradation-invariant Enhancement of Fundus Images via Pyramid Constraint Network
https://github.com/hey-it-s-me/corple
공식 코드https://github.com/hfut-cv/lmt-gp
공식 코드https://github.com/hikopensource/davar-lab-ocr
공식 코드SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition
https://github.com/hitzhangyu/FLW-Net
공식 코드Simplifying Low-Light Image Enhancement Networks with Relative Loss Functions
https://github.com/hitzhangyu/V2-Self-supervised-Image-Enhancement-Network-Training-With-Low-Light-Images-Only
공식 코드https://github.com/hitzhangyu/image-enhancement-with-denoise
공식 코드Better Than Reference In Low Light Image Enhancement: Conditional Re-Enhancement Networks
https://github.com/hluo29/rcnet
공식 코드RCNet: Deep Recurrent Collaborative Network for Multi-View Low-Light Image Enhancement
https://github.com/hongxiangharry/stochastic-iqt
공식 코드Low-field magnetic resonance image enhancement via stochastic image quality transfer
https://github.com/hou-guojia/udhtv
공식 코드Dual High-Order Total Variation Model for Underwater Image Restoration
https://github.com/hou-guojia/uid2021
공식 코드UID2021: An Underwater Image Dataset for Evaluation of No-reference Quality Assessment Metrics
https://github.com/hucvl/dark-burst-photography
공식 코드Burst Photography for Learning to Enhance Extremely Dark Images
https://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/icon-lab/AdaDiff
공식 코드Adaptive Diffusion Priors for Accelerated MRI Reconstruction
https://github.com/igor-morawski/nod
공식 코드https://github.com/igor-morawski/tmm-sem
공식 코드Leveraging Content and Context Cues for Low-Light Image Enhancement
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.