Controllable Image Enhancement
- URL: http://arxiv.org/abs/2206.08488v1
- Date: Thu, 16 Jun 2022 23:54:53 GMT
- Title: Controllable Image Enhancement
- Authors: Heewon Kim and Kyoung Mu Lee
- Abstract summary: We present a semiautomatic image enhancement algorithm that can generate high-quality images with multiple styles by controlling a few parameters.
An encoder-decoder framework encodes the retouching skills into latent codes and decodes them into the parameters of image signal processing functions.
- Score: 66.18525728881711
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Editing flat-looking images into stunning photographs requires skill and
time. Automated image enhancement algorithms have attracted increased interest
by generating high-quality images without user interaction. However, the
quality assessment of a photograph is subjective. Even in tone and color
adjustments, a single photograph of auto-enhancement is challenging to fit user
preferences which are subtle and even changeable. To address this problem, we
present a semiautomatic image enhancement algorithm that can generate
high-quality images with multiple styles by controlling a few parameters. We
first disentangle photo retouching skills from high-quality images and build an
efficient enhancement system for each skill. Specifically, an encoder-decoder
framework encodes the retouching skills into latent codes and decodes them into
the parameters of image signal processing (ISP) functions. The ISP functions
are computationally efficient and consist of only 19 parameters. Despite our
approach requiring multiple inferences to obtain the desired result,
experimental results present that the proposed method achieves state-of-the-art
performances on the benchmark dataset for image quality and model efficiency.
Related papers
- Quality-guided Skin Tone Enhancement for Portrait Photography [46.55401398142088]
We propose a quality-guided image enhancement paradigm that enables image enhancement models to learn the distribution of images with various quality ratings.
Our method can adjust the skin tone corresponding to different quality requirements.
arXiv Detail & Related papers (2024-06-22T13:36:30Z) - A Non-Uniform Low-Light Image Enhancement Method with Multi-Scale
Attention Transformer and Luminance Consistency Loss [11.585269110131659]
Low-light image enhancement aims to improve the perception of images collected in dim environments.
Existing methods cannot adaptively extract the differentiated luminance information, which will easily cause over-exposure and under-exposure.
We propose a multi-scale attention Transformer named MSATr, which sufficiently extracts local and global features for light balance to improve the visual quality.
arXiv Detail & Related papers (2023-12-27T10:07:11Z) - MSTRIQ: No Reference Image Quality Assessment Based on Swin Transformer
with Multi-Stage Fusion [8.338999282303755]
We propose a novel algorithm based on the Swin Transformer.
It aggregates information from both local and global features to better predict the quality.
It ranks 2nd in the no-reference track of NTIRE 2022 Perceptual Image Quality Assessment Challenge.
arXiv Detail & Related papers (2022-05-20T11:34:35Z) - Lightweight HDR Camera ISP for Robust Perception in Dynamic Illumination
Conditions via Fourier Adversarial Networks [35.532434169432776]
We propose a lightweight two-stage image enhancement algorithm sequentially balancing illumination and noise removal.
We also propose a Fourier spectrum-based adversarial framework (AFNet) for consistent image enhancement under varying illumination conditions.
Based on quantitative and qualitative evaluations, we also examine the practicality and effects of image enhancement techniques on the performance of common perception tasks.
arXiv Detail & Related papers (2022-04-04T18:48:51Z) - Universal and Flexible Optical Aberration Correction Using Deep-Prior
Based Deconvolution [51.274657266928315]
We propose a PSF aware plug-and-play deep network, which takes the aberrant image and PSF map as input and produces the latent high quality version via incorporating lens-specific deep priors.
Specifically, we pre-train a base model from a set of diverse lenses and then adapt it to a given lens by quickly refining the parameters.
arXiv Detail & Related papers (2021-04-07T12:00:38Z) - Towards Unsupervised Deep Image Enhancement with Generative Adversarial
Network [92.01145655155374]
We present an unsupervised image enhancement generative network (UEGAN)
It learns the corresponding image-to-image mapping from a set of images with desired characteristics in an unsupervised manner.
Results show that the proposed model effectively improves the aesthetic quality of images.
arXiv Detail & Related papers (2020-12-30T03:22:46Z) - Deep Image Compositing [93.75358242750752]
We propose a new method which can automatically generate high-quality image composites without any user input.
Inspired by Laplacian pyramid blending, a dense-connected multi-stream fusion network is proposed to effectively fuse the information from the foreground and background images.
Experiments show that the proposed method can automatically generate high-quality composites and outperforms existing methods both qualitatively and quantitatively.
arXiv Detail & Related papers (2020-11-04T06:12:24Z) - Early Exit or Not: Resource-Efficient Blind Quality Enhancement for
Compressed Images [54.40852143927333]
Lossy image compression is pervasively conducted to save communication bandwidth, resulting in undesirable compression artifacts.
We propose a resource-efficient blind quality enhancement (RBQE) approach for compressed images.
Our approach can automatically decide to terminate or continue enhancement according to the assessed quality of enhanced images.
arXiv Detail & Related papers (2020-06-30T07:38:47Z) - The Power of Triply Complementary Priors for Image Compressive Sensing [89.14144796591685]
We propose a joint low-rank deep (LRD) image model, which contains a pair of complementaryly trip priors.
We then propose a novel hybrid plug-and-play framework based on the LRD model for image CS.
To make the optimization tractable, a simple yet effective algorithm is proposed to solve the proposed H-based image CS problem.
arXiv Detail & Related papers (2020-05-16T08:17:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.