Photo Retouching
15 papers with code • 3 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Photo Retouching
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MIT-Adobe FiveK
IntHarmony
NILUT
Most implemented papers
Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement
caiyuanhao1998/retinexformer
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ICCV 2023
When enhancing low-light images, many deep learning algorithms are based on the Retinex theory.
CURL: Neural Curve Layers for Global Image Enhancement
sjmoran/CURL
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We present a novel approach to adjust global image properties such as colour, saturation, and luminance using human-interpretable image enhancement curves, inspired by the Photoshop curves tool.
Automatic Photo Adjustment Using Deep Neural Networks
Many photographic styles rely on subtle adjustments that depend on the image content and even its semantics.
Conditional Sequential Modulation for Efficient Global Image Retouching
hejingwenhejingwen/CSRNet
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ECCV 2020
The base network acts like an MLP that processes each pixel independently and the condition network extracts the global features of the input image to generate a condition vector.
STAR: A Structure-Aware Lightweight Transformer for Real-Time Image Enhancement
zzyfd/STAR-pytorch
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ICCV 2021
STAR is a general architecture that can be easily adapted to different image enhancement tasks.
High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network
Existing image-to-image translation (I2IT) methods are either constrained to low-resolution images or long inference time due to their heavy computational burden on the convolution of high-resolution feature maps.
PPR10K: A Large-Scale Portrait Photo Retouching Dataset with Human-Region Mask and Group-Level Consistency
HRP requires that more attention should be paid to human regions, while GLC requires that a group of portrait photos should be retouched to a consistent tone.
ABPN: Adaptive Blend Pyramid Network for Real-Time Local Retouching of Ultra High-Resolution Photo
The network is mainly composed of two components: a context-aware local retouching layer (LRL) and an adaptive blend pyramid layer (BPL).
MAXIM: Multi-Axis MLP for Image Processing
google-research/maxim
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CVPR 2022
In this work, we present a multi-axis MLP based architecture called MAXIM, that can serve as an efficient and flexible general-purpose vision backbone for image processing tasks.
Structure-Aware Flow Generation for Human Body Reshaping
jianqiangren/flowbasedbodyreshaping
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CVPR 2022
Body reshaping is an important procedure in portrait photo retouching.
