Image Enhancement Algorithm Based on Depth Difference and Illumination Adjustment

Li, Dan; Bao, Jin-An; Yuan, Sizhen; Wang, Hongdong; Wang, Likai; Liu, Weiwei

doi:10.1155/2021/6612471

Cited by 10 publications

(11 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the neural network has no specific scheme for finding the structure of neurons due to which the suitable and targeted results may not be achieved [ 19 ]. Moreover, in [ 20 ], the authors proposed an image enhancement method which was based on illumination tuning and the field variations of depth. This is attained and clustered by utilizing the principle of dark channel and spectral clustering techniques, respectively.…”

Section: Related Workmentioning

confidence: 99%

An Ensembled Spatial Enhancement Method for Image Enhancement in Healthcare

Ṣiddiqi

Alsirhani

2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

Most medical images are low in contrast because adequate details that may prove vital decisions are not visible to the naked eye. Also, due to the low-contrast nature of the image, it is not easily segmented because there is no significant change between the pixel values, which makes the gradient very small Hence, the contour cannot converge on the edges of the object. In this work, we have proposed an ensembled spatial method for image enhancement. In this ensembled approach, we first employed the Laplacian filter, which highlights the areas of fast intensity variation. This filter can determine the sufficient details of an image. The Laplacian filter will also improve those features having shrill disjointedness. Then, the gradient of the image has been determined, which utilizes the surrounding pixels for the weighted convolution operation for noise diminishing. However, in the gradient filter, there is one negative integer in the weighting. The intensity value of the middle pixel might be deducted from the surrounding pixels, to enlarge the difference between the head-to-head pixels for calculating the gradients. This is one of the reasons due to which the gradient filter is not entirely optimistic, which may be calculated in eight directions. Therefore, the averaging filter has been utilized, which is an effective filter for image enhancement. This approach does not rely on the values that are completely diverse from distinctive values in the surrounding due to which it recollects the details of the image. The proposed approach significantly showed the best performance on various images collected in dynamic environments.

show abstract

Section: Related Workmentioning

confidence: 99%

An Ensembled Spatial Enhancement Method for Image Enhancement in Healthcare

Ṣiddiqi

Alsirhani

2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…e existing technical solutions mainly include multiple image fusion and single image enhancement. is paper aims at the research of image enhancement [8].…”

Section: Literature Reviewmentioning

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

“…en, f(y) represents a complete spatial digitized image, and the assignment process is called the quantization process [8,9,19]. e image digitization process determines the size M and N of the processed image and the discrete grayscale count G of the pixel.…”

Section: Research and Optimization Of Image Preprocessing Technologymentioning

confidence: 99%

“…Finally, the authors conducted several experiments to verify the effect of the algorithm on different data sets. Li et al (2021) [9] put forward a traffic image enhancement model based on illumination adjustment and depth difference. e algorithm used the dark channel principle to obtain the image's depth of field and employed the spectral clustering algorithm to cluster the image's depth of field.…”

Section: Introductionmentioning

confidence: 99%

Film Effect Optimization by Deep Learning and Virtual Reality Technology in New Media Environment

Cui¹,

Zhang

Wang³

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Today, new media technology has widely penetrated art forms such as film and television, which has changed the way of visual expression in the new media environment. To better solve the problems of weak immersion, poor interaction, and low degree of simulation, the present work uses deep learning technology and virtual reality (VR) technology to optimize the film playing effect. Firstly, the optimized extremum median filter algorithm is used to optimize the “burr” phenomenon and a low compression ratio of the single video image. Secondly, the Generative Adversarial Network (GAN) in deep learning technology is used to enhance the data of the single video image. Finally, the decision tree algorithm and hierarchical clustering algorithm are used for the color enhancement of VR images. The experimental results show that the contrast of a single-frame image optimized by this system is 4.21, the entropy is 8.66, and the noise ratio is 145.1, which shows that this method can effectively adjust the contrast parameters to prevent the loss of details and reduce the dazzling intensity. The quality and diversity of the specific types of images generated by the proposed GAN are improved compared with the current mainstream GAN method with supervision, which is in line with the subjective evaluation results of human beings. The Frechet Inception Distance value is also significantly improved compared with Self-Attention Generative Adversarial Network. It shows that the sample generated by the proposed method has precise details and rich texture features. The proposed scheme provides a reference for optimizing the interactivity, immersion, and simulation of VR film.

show abstract

Image Enhancement Algorithm Based on Depth Difference and Illumination Adjustment

Cited by 10 publications

References 43 publications

An Ensembled Spatial Enhancement Method for Image Enhancement in Healthcare

An Ensembled Spatial Enhancement Method for Image Enhancement in Healthcare

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

Film Effect Optimization by Deep Learning and Virtual Reality Technology in New Media Environment

Contact Info

Product

Resources

About