computer-vision-7

Photo Retouching

3260 papers • 126 benchmarks • 313 datasets

This task has no description! Would you like to contribute one?

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in photo-retouching-7

Trend

Dataset

Best Model

Actions

MIT-Adobe 5k

MIT-Adobe 5k (480p)

MIT-Adobe 5k (1080p)

Libraries

i

Use these libraries to find photo-retouching-7 models and implementations

Datasets

MIT-Adobe FiveK

IntHarmony

NILUT

Subtasks

No subtasks available.

Most implemented papers

Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement

Radu Timofte, Haoqian Wang, Jing Lin, Yulun Zhang, Hao Bian•Sat Mar 11 2023

A simple yet principled One-stage Retinex-based Framework (ORF), designed an Illumination-Guided Transformer (IGT) that utilizes illumination representations to direct the modeling of non-local interactions of regions with different lighting conditions, and obtains the algorithm, Retinexformer.

493

Content

MIT-Adobe 5k (1080p)

0

Paper Graph

Automatic Photo Adjustment Using Deep Neural Networks

Zhicheng Yan, Yizhou Yu, H. Zhang, Baoyuan Wang, Sylvain Paris•Tue Dec 23 2014

This article forms automatic photo adjustment in a manner suitable for deep neural networks, and introduces an image descriptor accounting for the local semantics of an image that can model local adjustments that depend on image semantics.

286 0

Paper Graph

CURL: Neural Curve Layers for Global Image Enhancement

G. Slabaugh, S. Moran, Steven G. McDonagh•Thu Nov 28 2019

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. Our method, dubbed neural CURve Layers (CURL), is designed as a multi-colour space neural retouching block trained jointly in three different colour spaces (HSV, CIELab, RGB) guided by a novel multi-colour space loss. The curves are fully differentiable and are trained end-to-end for different computer vision problems including photo enhancement (RGB-to-RGB) and as part of the image signal processing pipeline for image formation (RAW-to-RGB). To demonstrate the effectiveness of CURL we combine this global image transformation block with a pixel-level (local) image multi-scale encoder-decoder backbone network. In an extensive experimental evaluation we show that CURL produces state-of-the-art image quality versus recently proposed deep learning approaches in both objective and perceptual metrics, setting new state-of-the-art performance on multiple public datasets. Our code is publicly available at: https://github.com/sjmoran/CURL.

71 0

Paper Graph

Conditional Sequential Modulation for Efficient Global Image Retouching

Chao Dong, Y. Qiao, Yihao Liu, Jingwen He•Mon Sep 21 2020

An extremely light-weight framework - Conditional Sequential Retouching Network (CSRNet) - for efficient global image retouching is proposed that achieves state-of-the-art performance on the benchmark MIT-Adobe FiveK dataset quantitively and qualitatively.

143 0

Paper Graph

STAR: A Structure-aware Lightweight Transformer for Real-time Image Enhancement

Ping Luo, Yitong Jiang, Jun Jiang, Jinwei Gu, Zhaoyang Zhang, Xiaogang Wang•Thu Sep 30 2021

Image and video enhancement such as color constancy, low light enhancement, and tone mapping on smartphones is challenging, because high-quality images should be achieved efficiently with a limited resource budget. Unlike prior works that either used very deep CNNs or large Trans-former models, we propose a structure-aware lightweight Transformer, termed STAR, for real-time image enhancement. STAR is formulated to capture long-range dependencies between image patches, which naturally and implicitly captures the structural relationships of different regions in an image. STAR is a general architecture that can be easily adapted to different image enhancement tasks. Extensive experiments show that STAR can effectively boost the quality and efficiency of many tasks such as illumination enhancement, auto white balance, and photo retouching, which are indispensable components for image processing on smartphones. For example, STAR reduces model complexity and improves image quality compared to the recent state-of-the-art [19] on the MIT-Adobe FiveK dataset [7] (i.e., 1.8dB PSNR improvements with 25% parameters and 13% float operations.)

115 0

Paper Graph

High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network

Jie Liang, Huiyu Zeng, Lei Zhang•Tue May 18 2021

A Laplacian Pyramid Translation Network (LPTN) is proposed to simultaneously perform these two tasks, where it is revealed that the attribute transformations, such as illumination and color manipulation, relate more to the low-frequency component, while the content details can be adaptively refined on high-frequency components.

120 0

Paper Graph

PPR10K: A Large-Scale Portrait Photo Retouching Dataset with Human-Region Mask and Group-Level Consistency

Jie Liang, Huiyu Zeng, Miaomiao Cui, Xuansong Xie, Lei Zhang•Tue May 18 2021

The constructed PPR10K dataset provides a good bench-mark for studying automatic PPR methods, and experiments demonstrate that the proposed learning strategies are effective to improve the retouching performance.

67 0

Paper Graph

ABPN: Adaptive Blend Pyramid Network for Real-Time Local Retouching of Ultra High-Resolution Photo

Miaomiao Cui, Xuansong Xie, Xiefan Guo, Hongyu Yang, Biwen Lei, Dihe Huang•Tue May 31 2022

Photo retouching finds many applications in various fields. However, most existing methods are designed for global retouching and seldom pay attention to the local region, while the latter is actually much more tedious and time-consuming in photography pipelines. In this paper, we propose a novel adaptive blend pyramid network, which aims to achieve fast local retouching on ultra high-resolution photos. The network is mainly composed of two components: a context-aware local retouching layer (LRL) and an adaptive blend pyramid layer (BPL). The LRL is designed to implement local retouching on low-resolution images, giving full consideration of the global context and local texture information, and the BPL is then developed to progressively expand the low-resolution results to the higher ones, with the help of the proposed adaptive blend module and refining module. Our method outperforms the existing methods by a large margin on two local photo retouching tasks and exhibits excellent performance in terms of running speed, achieving real-time inference on 4K images with a single NVIDIA Tesla P100 GPU. Moreover, we introduce the first high-definition cloth retouching dataset CRHD-3K to promote the research on local photo retouching. The dataset is available at https://github.com/youngLbw/crhd-3K.

19 0

Paper Graph

MAXIM: Multi-Axis MLP for Image Processing

Hossein Talebi, P. Milanfar, Zhengzhong Tu, Feng Yang, A. Bovik, Yinxiao Li, Han Zhang•Sat Jan 08 2022

The proposed MAXIM model achieves state-of-the-art performance on more than ten benchmarks across a range of image processing tasks, including denoising, deblurring, de raining, dehazing, and enhancement while requiring fewer or comparable numbers of parameters and FLOPs than competitive models.

614 0

Paper Graph

Structure-Aware Flow Generation for Human Body Reshaping

Miaomiao Cui, Xuansong Xie, Yuan Yao, Biwen Lei, Jianqiang Ren•Tue Mar 08 2022

An end-to-end flow generation architecture under the guidance of body structural priors is formulating to achieve unprecedentedly controllable performance under arbitrary poses and garments and significantly outperforms existing state-of-the-art methods in terms of visual performance, controllability, and efficiency.

9 0

Paper Graph

Adding a benchmark result helps the community track progress.