Enhancing Underexposed Photos Using Perceptually Bidirectional Similarity

Zhang, Qing; Nie, Yongwei; Zhu, Lei; Xiao, Chunxia; Zheng, Wei‐Shi

doi:10.1109/tmm.2020.2982045

Cited by 26 publications

(7 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the application of image enhancement, it can achieve good color retention and enhancement performance at the same time [21,22]. HSI color model is completely different from the above two color models based on physics or process, which is -a perception based color model [23]. is color model can not only avoid the phenomenon of color deviation but also simplify the processing of color images to a great extent.…”

Section: Color Modelmentioning

confidence: 99%

Optimization of Plane Image Color Enhancement Processing Based on Computer Vision Virtual Reality

Xue

Ming

Lian

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In order to solve the problems of low brightness contrast of a color image, hiding a large amount of detail information, and deviation of color information in the process of image acquisition, an optimization method of plane image color enhancement processing based on computer vision virtual reality is proposed. In this method, the input RGB image is converted into the image represented by the HSI color model, and its adaptive brightness is adjusted to improve the overall brightness of the image. For the local detail enhancement of the color image, the three-dimensional Gaussian model perceived by retinal neurons is introduced into the illuminance image estimation of the MSR algorithm to enhance the image color. The results are as follows: from the perspective of objective parameter evaluation, the mean, standard deviation, information entropy, and average gradient of example images 1 and 2 are improved by about 70%; this algorithm not only enhances the brightness and contrast of the image but also maintains the detailed edge information of the image and the color characteristics of the object itself. The average enhancement rate is the highest among various algorithms, up to 95%. The algorithm proposed in this paper maintains the edge detail information of the image, optimizes the defects of the combination of traditional bilateral filtering and Retinex algorithm, and the color is also well restored, which makes the monitoring image easier to identify, more conducive to criminal investigation and solving cases, and lays a foundation for subsequent image processing.

show abstract

Section: Color Modelmentioning

confidence: 99%

Optimization of Plane Image Color Enhancement Processing Based on Computer Vision Virtual Reality

Xue

Ming

Lian

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…[GS20] divided RGB‐space into multiple regions, employing various geometric techniques to separate pixel colors depending on their position within the RGB‐space. [ZNZ * 21] formulated an optimization solving for palette colors and mixing weights simultaneously by considering color separation priors. [JYS19] create palettes in a hierarchical, bottom‐up manner.…”

Section: Related Workmentioning

confidence: 99%

LoCoPalettes: Local Control for Palette‐based Image Editing

Chao

Klein

Tan

et al. 2023

Computer Graphics Forum

View full text Add to dashboard Cite

Palette‐based image editing takes advantage of the fact that color palettes are intuitive abstractions of images. They allow users to make global edits to an image by adjusting a small set of colors. Many algorithms have been proposed to compute color palettes and corresponding mixing weights. However, in many cases, especially in complex scenes, a single global palette may not adequately represent all potential objects of interest. Edits made using a single palette cannot be localized to specific semantic regions. We introduce an adaptive solution to the usability problem based on optimizing RGB palette colors to achieve arbitrary image‐space constraints and automatically splitting the image into semantic sub‐regions with more representative local palettes when the constraints cannot be satisfied. Our algorithm automatically decomposes a given image into a semantic hierarchy of soft segments. Difficult‐to‐achieve edits become straightforward with our method. Our results show the flexibility, control, and generality of our method.

show abstract

“…This problem has been widely studied, since the presence of noise would not only significantly degrade the perceptual quality of an image, but also may adversely affect the performance of many fundamental tasks, e.g. , object detection [TPL20, CMS*20], tracking [BDGT19, CYZ*21], and image enhancement [ZNZ19, ZNZ*20, WZF*19,ZYX*18,ZNZX15].…”

Section: Introductionmentioning

confidence: 99%

Learning Multi‐Scale Deep Image Prior for High‐Quality Unsupervised Image Denoising

Jiang

Zhang

Nie

et al. 2022

Computer Graphics Forum

Self Cite

View full text Add to dashboard Cite

Recent methods on image denoising have achieved remarkable progress, benefiting mostly from supervised learning on massive noisy/clean image pairs and unsupervised learning on external noisy images. However, due to the domain gap between the training and testing images, these methods typically have limited applicability on unseen images. Although several attempts have been made to avoid the domain gap issue by learning denoising from singe noisy image itself, they are less effective in handling real‐world noise because of assuming the noise corruptions are independent and zero mean. In this paper, we go step further beyond prior work by presenting a novel unsupervised image denoising framework trained from single noisy image without making any explicit assumptions on the noise statistics. Our approach is built upon the deep image prior (DIP), which enables diverse image restoration tasks. However, as is, the denoising performance of DIP will significantly deteriorate on nonzero‐mean noise and is sensitive to the number of iterations. To overcome this problem, we propose to utilize multi‐scale deep image prior by imposing DIP across different image scales under the constraint of a scale consistency. Experiments on synthetic and real datasets demonstrate that our method performs favorably against the state‐of‐the‐art methods for image denoising.

show abstract

Enhancing Underexposed Photos Using Perceptually Bidirectional Similarity

Cited by 26 publications

References 71 publications

Optimization of Plane Image Color Enhancement Processing Based on Computer Vision Virtual Reality

Optimization of Plane Image Color Enhancement Processing Based on Computer Vision Virtual Reality

LoCoPalettes: Local Control for Palette‐based Image Editing

Learning Multi‐Scale Deep Image Prior for High‐Quality Unsupervised Image Denoising

Contact Info

Product

Resources

About