34‐1: Evaluating and Minimizing Color Distortion in Wide‐Gamut Displays Due to Variations of Cone Fundamentals among Color‐Normal Observers

2021 ◽  
Vol 52 (1) ◽  
pp. 450-453
Author(s):  
Lorne Whitehead ◽  
Kevin A.G. Smet ◽  
Michael Webster
Keyword(s):  
Color Distortion

Underwater Image Enhancement Method Combining color and Luminance

2020 ◽  
Vol 13 ◽  
Author(s):  
ZHAO Baiting ◽  
WANG Feng ◽  
JIA Xiaofen ◽  
GUO Yongcun ◽  
WANG Chengjun
Keyword(s):  
Image Enhancement ◽  
Color Space ◽  
Low Frequency ◽  
Brightness Enhancement ◽  
Color Distortion ◽  
Underwater Image ◽  
Enhancement Method ◽  
Frequency Components ◽  
Image Contour ◽  
Objective Indicators

Background:: Aiming at the problems of color distortion, low clarity and poor visibility of underwater image caused by complex underwater environment, a wavelet fusion method UIPWF for underwater image enhancement is proposed. Methods:: First of all, an improved NCB color balance method is designed to identify and cut the abnormal pixels, and balance the color of R, G and B channels by affine transformation. Then, the color correction map is converted to CIELab color space, and the L component is equalized with contrast limited adaptive histogram to obtain the brightness enhancement map. Finally, different fusion rules are designed for low-frequency and high-frequency components, the pixel level wavelet fusion of color balance image and brightness enhancement image is realized to improve the edge detail contrast on the basis of protecting the underwater image contour. Results:: The experiments demonstrate that compared with the existing underwater image processing methods, UIPWF is highly effective in the underwater image enhancement task, improves the objective indicators greatly, and produces visually pleasing enhancement images with clear edges and reasonable color information. Conclusion:: The UIPWF method can effectively mitigate the color distortion, improve the clarity and contrast, which is applicable for underwater image enhancement in different environments.

scholarly journals Underwater Image Enhancement Based on Local Contrast Correction and Multi-Scale Fusion

2021 ◽  
Vol 9 (2) ◽  
pp. 225
Author(s):  
Farong Gao ◽  
Kai Wang ◽  
Zhangyi Yang ◽  
Yejian Wang ◽  
Qizhong Zhang
Keyword(s):  
Image Enhancement ◽  
Local Contrast ◽  
Fusion Method ◽  
Formation Model ◽  
Low Contrast ◽  
White Balance ◽  
Multi Scale ◽  
Color Distortion ◽  
Underwater Image ◽  
Enhancement Method

In this study, an underwater image enhancement method based on local contrast correction (LCC) and multi-scale fusion is proposed to resolve low contrast and color distortion of underwater images. First, the original image is compensated using the red channel, and the compensated image is processed with a white balance. Second, LCC and image sharpening are carried out to generate two different image versions. Finally, the local contrast corrected images are fused with sharpened images by the multi-scale fusion method. The results show that the proposed method can be applied to water degradation images in different environments without resorting to an image formation model. It can effectively solve color distortion, low contrast, and unobvious details of underwater images.

scholarly journals An image dehazing algorithm based on double priors constraint

2018 ◽  
Vol 189 ◽  
pp. 04009
Author(s):  
Kun Liu ◽  
Shiping Wang ◽  
Linyuan He ◽  
Duyan Bi ◽  
Shan Gao
Keyword(s):  
Standard Deviation ◽  
Mean Value ◽  
Experimental Results ◽  
Dark Channel Prior ◽  
Image Dehazing ◽  
Color Distortion ◽  
Local Media ◽  
Dark Channel

Aiming at the color distortion of the restored image in the sky region, we propose an image dehazing algorithm based on double priors constraint. Firstly, we divided the haze image into sky and non-sky regions. Then the Color-lines prior and dark channel prior are used for estimating the transmission of sky and non-sky regions respectively. After introducing color-lines prior to correct sky regions restored by the dark channel prior, we get an accurate transmission. Finally, the local media mean value and standard deviation are used to refine the transmission to obtain the dehazing image. Experimental results show that the algorithm has obvious advantages in the recovery of the sky area.

An Ameliorative Defogging Algorithm Based on the MSR

2014 ◽  
Vol 556-562 ◽  
pp. 3483-3486 ◽  
Author(s):  
Ming Liu ◽  
Chao Xu
Keyword(s):  
Adaptive Filter ◽  
Visual Effect ◽  
Existing Problems ◽  
Multi Scale ◽  
Color Distortion ◽  
Homomorphic Filtering ◽  
Image Details

In view of the existing problems of traditional defogging algorithms,such as color distortion and halo phenomenon in MSR algorithm (multi-scale Retinex) , we improve it by using adaptive filter and different weighting factor.In other word,when gray levels of pixels within a certain threshold, use homomorphic filtering; otherwise use different state filtering device. Experiments shown that image details are enhanced, and contrast is enhanced, visual effect is improved.

scholarly journals FD-GAN: Generative Adversarial Networks with Fusion-Discriminator for Single Image Dehazing

2020 ◽  
Vol 34 (07) ◽  
pp. 10729-10736 ◽  
Author(s):  
Yu Dong ◽  
Yihao Liu ◽  
He Zhang ◽  
Shifeng Chen ◽  
Yu Qiao
Keyword(s):  
Training Data ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Single Image ◽  
Image Dehazing ◽  
Atmospheric Scattering ◽  
Color Distortion ◽  
Adversarial Networks ◽  
End To End ◽  
Single Image Dehazing

Recently, convolutional neural networks (CNNs) have achieved great improvements in single image dehazing and attained much attention in research. Most existing learning-based dehazing methods are not fully end-to-end, which still follow the traditional dehazing procedure: first estimate the medium transmission and the atmospheric light, then recover the haze-free image based on the atmospheric scattering model. However, in practice, due to lack of priors and constraints, it is hard to precisely estimate these intermediate parameters. Inaccurate estimation further degrades the performance of dehazing, resulting in artifacts, color distortion and insufficient haze removal. To address this, we propose a fully end-to-end Generative Adversarial Networks with Fusion-discriminator (FD-GAN) for image dehazing. With the proposed Fusion-discriminator which takes frequency information as additional priors, our model can generator more natural and realistic dehazed images with less color distortion and fewer artifacts. Moreover, we synthesize a large-scale training dataset including various indoor and outdoor hazy images to boost the performance and we reveal that for learning-based dehazing methods, the performance is strictly influenced by the training data. Experiments have shown that our method reaches state-of-the-art performance on both public synthetic datasets and real-world images with more visually pleasing dehazed results.

An IHS-Based Fusion for Color Distortion Reduction and Vegetation Enhancement in IKONOS Imagery

2011 ◽  
Vol 49 (5) ◽  
pp. 1590-1602 ◽  
Author(s):  
Miloud Chikr El-Mezouar ◽  
Nasreddine Taleb ◽  
Kidiyo Kpalma ◽  
Joseph Ronsin
Keyword(s):  
Color Distortion ◽  
Distortion Reduction

scholarly journals Color calibration of an RGB camera mounted in front of a microscope with strong color distortion

2013 ◽  
Vol 52 (21) ◽  
pp. 5262 ◽  
Author(s):  
Renée Charrière ◽  
Mathieu Hébert ◽  
Alain Trémeau ◽  
Nathalie Destouches
Keyword(s):  
Color Distortion ◽  
Color Calibration
Sign in / Sign up

Export Citation Format

Share Document