Multi-scale exposure fusion via gradient domain guided image filtering

2017 ◽  
Author(s):  
Fei Kou ◽  
Zhengguo Li ◽  
Changyun Wen ◽  
Weihai Chen
Keyword(s):  
Image Filtering ◽  
Exposure Fusion ◽  
Multi Scale ◽  
Gradient Domain ◽  
Guided Image Filtering

Efficient local stereo matching algorithm based on fast gradient domain guided image filtering

2021 ◽  
Vol 95 ◽  
pp. 116280
Author(s):  
Weimin Yuan ◽  
Cai Meng ◽  
Xiaoyan Tong ◽  
Zhaoxi Li
Keyword(s):  
Stereo Matching ◽  
Image Filtering ◽  
Matching Algorithm ◽  
Gradient Domain ◽  
Fast Gradient ◽  
Guided Image Filtering

scholarly journals Low-Light Image Brightening via Fusing Additional Virtual Images

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4614 ◽  
Author(s):  
Yi Yang ◽  
Zhengguo Li ◽  
Shiqian Wu
Keyword(s):  
Computational Photography ◽  
Least Square ◽  
Low Light ◽  
Edge Preserving ◽  
Exposure Fusion ◽  
Intensity Mapping ◽  
Gradient Domain ◽  
Virtual Images ◽  
Relative Brightness ◽  
Guided Image Filtering

Capturing high-quality images via mobile devices in low-light or backlighting conditions is very challenging. In this paper, a new, single image brightening algorithm is proposed to enhance an image captured in low-light conditions. Two virtual images with larger exposure times are generated to increase brightness and enhance fine details of the underexposed regions. In order to reduce the brightness change, the virtual images are generated via intensity mapping functions (IMFs) which are computed using available camera response functions (CRFs). To avoid possible color distortion in the virtual image due to one-to-many mapping, a least square minimization problem is formulated to determine brightening factors for all pixels in the underexposed regions. In addition, an edge-preserving smoothing technique is adopted to avoid noise in the underexposed regions from being amplified in the virtual images. The final brightened image is obtained by fusing the original image and two virtual images via a gradient domain guided image filtering (GGIF) based multiscale exposure fusion (MEF) with properly defined weights for all the images. Experimental results show that the relative brightness and color are preserved better by the proposed algorithm. The details in bright regions are also preserved well in the final image. The proposed algorithm is expected to be useful for computational photography on smart phones.

scholarly journals A Nonlinear Gradient Domain-Guided Filter Optimized by Fractional-Order Gradient Descent with Momentum RBF Neural Network for Ship Image Dehazing

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Qionglin Fang ◽  
X. U. E. Han
Keyword(s):  
Neural Network ◽  
Fractional Order ◽  
Gradient Descent ◽  
Rbf Neural Network ◽  
Similarity Index ◽  
Image Filtering ◽  
Image Dehazing ◽  
Filtering Algorithm ◽  
Gradient Domain ◽  
Guided Image Filtering

To avoid the blurred edges, noise, and halos caused by guided image filtering algorithm, this paper proposed a nonlinear gradient domain-guided image filtering algorithm for image dehazing. To dynamically adjust the edge preservation and smoothness of dehazed images, this paper proposed a fractional-order gradient descent with momentum RBF neural network to optimize the nonlinear gradient domain-guided filtering (NGDGIF-FOGDMRBF). Its convergence is proved. In order to speed up the convergence process, an adaptive learning rate is used to adjust the training process reasonably. The results verify the theoretical results of the proposed algorithm such as its monotonicity and convergence. The descending curve of error values by FOGDM is smoother than gradient descent and gradient descent with momentum method. The influence of regularization parameter is analyzed and compared. Compared with dark channel prior, histogram equalization, homomorphic filtering, and multiple exposure fusion, the halo and noise generated are significantly reduced with higher peak signal-to-noise ratio and structural similarity index.

Pan-GGF: A probabilistic method for pan-sharpening with gradient domain guided image filtering

2019 ◽  
Vol 156 ◽  
pp. 177-190 ◽  
Author(s):  
Peixian Zhuang ◽  
Qingshan Liu ◽  
Xinghao Ding
Keyword(s):  
Probabilistic Method ◽  
Image Filtering ◽  
Gradient Domain ◽  
Guided Image Filtering

scholarly journals A Single Image Dehazing Technique Using the Dual Transmission Maps Strategy and Gradient-Domain Guided Image Filtering

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Syed Muhammad Ehsan ◽  
Muhammad Imran ◽  
Anayat Ullah ◽  
Ersin Elbasi
Keyword(s):  
Image Filtering ◽  
Single Image ◽  
Image Dehazing ◽  
Gradient Domain ◽  
Guided Image Filtering ◽  
Dual Transmission ◽  
Single Image Dehazing
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