Exposure

By Yuanming Hu, Hao He, Chenxi Xu, Baoyuan Wang, Stephen Lin. ACM Transactions on Graphics, 2017

401 citations recorded by the source corpus.

Retouching can significantly elevate the visual appeal of photos, but many casual photographers lack the expertise to do this well. To address this problem, previous works have proposed automatic retouching systems based on supervised learning from paired training images acquired before and after manual editing. As it is difficult for users to acquire paired images that reflect their retouching preferences, we present in this article a deep learning approach that is instead trained on unpaired data, namely, a set of photographs that exhibits a retouching style the user likes, which is much easier to collect. Our system is formulated using deep convolutional neural networks that learn to apply different retouching operations on an input image. Network training with respect to various types of edits is enabled by modeling these retouching operations in a unified manner as resolution-independent differentiable filters. To apply the filters in a proper sequence and with suitable parameters, we employ a deep reinforcement learning approach that learns to make decisions on what action to take next, given the current state of the image. In contrast to many deep learning systems, ours provides users with an understandable solution in the form of conventional retouching edits rather than just a “black-box” result. Through quantitative comparisons and user studies, we show that this technique generates retouching results consistent with the provided photo set.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/18624a7c5b009b831aec15c931b9f2ff01755f44

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

Citations in the corpus

MAXIM: Multi-Axis MLP for Image Processing (2022, methodology): “Input Target CycleGAN [121] Exposure [31] DPE [18] EnlightenGAN UEGAN [63] MAXIM-2S”
The Interplay between Intuition and Rationality in Strategic Decision Making: A Paradox Perspective (2017, neutral): “By structuring sometimes complex and extensive information in simple and engaging ways, visualizations facilitate information absorption and affective reactions 2 that trigger new intuitive connections.”
Learning to Restore Low-Light Images via Decomposition-and-Enhancement (2020, methodology): “Recently, there are also some enhancement methods [6, 22] proposed to directly retouch the camera raw data into high quality output images.”
DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks (2017, methodology): “In [25] the authors proposed an algorithm for automatic exposure correction using hand-designed features and predeﬁned rules.”
Underexposed Photo Enhancement Using Deep Illumination Estimation (2019, methodology): “We compare our method with the following five state-ofthe-art image enhancement methods: (i) the latest Retinexbased method, JieP [4], and (ii)-(v) four recent deeplearning-based methods of HDRNet [13], DPE [9], White- Box [15], and Distort-and-Recover [22].”