Real-time haze removal in monocular images using locally adaptive processing

Díaz-Ramírez, Víctor H.; Hernandez-Beltran, Jose Enrique; Juarez-Salazar, Rigoberto

doi:10.1007/s11554-017-0698-z

Cited by 9 publications

(9 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Qualitative comparison of results on some challenging images: (a) Original hazy images, (b)–(f) dehazed images by DCP [13], CAP [16], MOF [18], LAP [35], and our proposed algorithm…”

Section: Resultsmentioning

confidence: 99%

“…The computer used in the experiment had an Intel(R) Core (TM) i7-6700 CPU, with a frequency of 3.4 GHz, and 8 GB RAM. We compared the proposed algorithm with the following four popular algorithms for single-image haze removal: DCP [13], CAP [16], MOF [18], and LAP [35].…”

Section: Resultsmentioning

confidence: 99%

“…Qualitative comparison of results on some challenging images: (a) Original hazy images, (b)-(f) dehazed images by DCP [13], CAP [16], MOF [18], LAP [35], and our proposed algorithm suffer significantly from over-enhancement. For example, the forest in the third image and the rocks in the bottom right of the fourth image are much brighter than they should be.…”

Section: Figurementioning

confidence: 99%

See 2 more Smart Citations

An improved algorithm using weighted guided coefficient and union self‐adaptive image enhancement for single image haze removal

Zhou

et al. 2021

IET Image Processing

View full text Add to dashboard Cite

The visibility of outdoor images is usually significantly degraded by haze. Existing dehazing algorithms, such as dark channel prior (DCP) and colour attenuation prior (CAP), have made great progress and are highly effective. However, they all suffer from the problems of dark distortion and detailed information loss. This paper proposes an improved algorithm for single-image haze removal based on dark channel prior with weighted guided coefficient and union self-adaptive image enhancement. First, a weighted guided coefficient method with sampling based on guided image filtering is proposed to refine the transmission map efficiently. Second, the k-means clustering method is adopted to calibrate the original image into bright and non-bright colour areas and form a transmission constraint matrix. The constraint matrix is then marked by connected-component labelling, and small bright regions are eliminated to form an atmospheric light constraint matrix, which can suppress the halo effect and optimize the atmospheric light. Finally, an adaptive linear contrast enhancement algorithm with a union score is proposed to optimize restored images. Experimental results demonstrate that the proposed algorithm can overcome the problems of image distortion and detailed information loss and is more efficient than conventional dehazing algorithms.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

“…Qualitative comparison of results on some challenging images: (a) Original hazy images, (b)–(f) dehazed images by DCP [13], CAP [16], MOF [18], LAP [35], and our proposed algorithm…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

See 1 more Smart Citation

An improved algorithm using weighted guided coefficient and union self‐adaptive image enhancement for single image haze removal

Zhou

et al. 2021

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…The study in [34] uses locally adaptive neighbourhood and calculates order statistics. By using this information, it produces the transmission map and handles the haze-free image.…”

Section: Figure 2 Atmospheric Light Scattering Modelmentioning

confidence: 99%

Smart and real-time image dehazing on mobile devices

Çimtay

2021

J Real-Time Image Proc

View full text Add to dashboard Cite

Haze is one of the common factors that degrades the visual quality of the images and videos. This diminishes contrast and reduces visual efficiency. The ALS (Atmospheric light scattering) model which has two unknowns to be estimated from the scene: atmospheric light and transmission map, is commonly used for dehazing. The process of modelling the atmospheric light scattering is complex and estimation of scattering is time consuming. This condition makes dehazing in real-time difficult. In this work, a new approach is employed for dehazing in real-time which reads the orientation sensor of mobile device and compares the amount of rotation with a pre-specified threshold. The system decides whether to recalculate the atmospheric light or not. When the amount of rotation is little means there are only subtle changes to the scene, it uses the pre-estimated atmospheric light. Therefore, the system does not need to recalculate it at each time instant and this approach accelerates the overall dehazing process. 0.07s fps (frame per second) per frame processing time (~15fps) is handled for 360p imagery. Frame processing time results show that our approach is superior to the state-of-the-art real-time dehazing implementations on mobile operating systems.

show abstract

“…Correlation filters (CF) are used in pattern recognition due to following advantages [20], [27], [45], [46]: a) invariant to noise, shift, and variable illumination, b) high ability for discrimination, c) ability to use both content and shape, d) can simultaneously detect and locate, and e) ability to continually adapt to changes in appearance. Correlation filters can be applied to face detection [47], [48], tracking [49]- [51] and recognition [52].…”

Section: Correlation Filters As Facial Descriptormentioning

confidence: 99%

Target Field of View Prediction Using Artificial Pheromones for People Reidentification

et al. 2019

View full text Add to dashboard Cite

People reidentification is a fundamental task in automated video surveillance based on computer vision. Reidentification happens when a person seen in a field of view is the same that has been observed in other fields of view. A person who has disappeared from one field of view can appear in any other within a camera network. Instead of looking for the person in all neighboring fields of view, for an intelligent video surveillance system, it is more practical to predict which of the neighboring camera views the person could appear. This prediction can become achieved by learning the paths the person usually follows in the camera network. The ant colony optimization technique has properties that can get exploited for this purpose; precisely, the accumulation and evaporation of artificial pheromones are used to learn the paths. After the learning process, the proposed method can make predictions every time that the person leaves a field of view. Such prediction is evaluated to obtain feedback and further tune the learning process. The path followed by the person becomes obtained by tracking their face image within and between fields of view using correlation filters as descriptors. The results obtained from an extensive experiment show that the field of view that the person selects to visit can be successfully predicted using artificial pheromones, and thus, reduce the resources that require reidentification.

show abstract

Real-time haze removal in monocular images using locally adaptive processing

Cited by 9 publications

References 22 publications

An improved algorithm using weighted guided coefficient and union self‐adaptive image enhancement for single image haze removal

An improved algorithm using weighted guided coefficient and union self‐adaptive image enhancement for single image haze removal

Smart and real-time image dehazing on mobile devices

Target Field of View Prediction Using Artificial Pheromones for People Reidentification

Contact Info

Product

Resources

About