scholarly journals Gray-Scale Image Dehazing Guided by Scene Depth Information

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Bo Jiang ◽  
Wanxu Zhang ◽  
Jian Zhao ◽  
Yi Ru ◽  
Min Liu ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Physical Model ◽  
Color Image ◽  
Depth Information ◽  
Gray Scale ◽  
Image Dehazing ◽  
Guided Image Filter ◽  
Gray Scale Image ◽  
Novel Method ◽  
Scene Depth

Combined with two different types of image dehazing strategies based on image enhancement and atmospheric physical model, respectively, a novel method for gray-scale image dehazing is proposed in this paper. For image-enhancement-based strategy, the characteristics of its simplicity, effectiveness, and no color distortion are preserved, and the common guided image filter is modified to match the application of image enhancement. Through wavelet decomposition, the high frequency boundary of original image is preserved in advance. Moreover, the process of image dehazing can be guided by the image of scene depth proportion directly estimated from the original gray-scale image. Our method has the advantages of brightness consistency and no distortion over the state-of-the-art methods based on atmospheric physical model. Particularly, our method overcomes the essential shortcoming of the abovementioned methods that are mainly working for color image. Meanwhile, an image of scene depth proportion is acquired as a byproduct of image dehazing.

Application of image enhancement and pattern recognition algorithms to seismic reflection data

1983 ◽  
Vol 73 (1) ◽  
pp. 307-314
Author(s):  
George A. McMechan
Keyword(s):  
Pattern Recognition ◽  
Pacific Ocean ◽  
Image Enhancement ◽  
Seismic Reflection ◽  
Gray Scale ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Recognition Algorithms ◽  
Gray Scale Image ◽  
Northeastern Pacific

abstract A digital seismic reflection section may be converted to a gray scale image composed of pixels and processed with techniques borrowed from the disciplines of image enhancement and pattern recognition. Types of processing include scaling, thresholding, density equalization, filtering, segmentation, and edge-finding. These are successfully applied to a migrated common mid-point seismic reflection line that traverses the Queen Charlotte fault (located in the northeastern Pacific Ocean). The result is the definition and enhancement of an elongated, near-vertical reflectivity anomaly associated with the Queen Charlotte fault.

An Improved Color Image Enhancement Algorithm Based on 3-D Color Histogram Equalization

2013 ◽  
Vol 321-324 ◽  
pp. 1133-1137
Author(s):  
Yu Ting Song ◽  
Xiu Hua Ji ◽  
Shi Lin Zhao
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Histogram Equalization ◽  
Color Images ◽  
Color Histogram ◽  
Test Results ◽  
Gray Scale ◽  
Color Image Enhancement ◽  
Equalization Algorithm ◽  
Enhancement Algorithm

This paper proposes an improved color image enhancement algorithm based on 3-D color histogram equalization algorithm. When the existed 3-D color histogram equalization algorithms in the literatures are applied in processing dim color images, the processed color images often turn pale due to the decrease of color-saturations and have false contours due to gray-scale merging phenomenon in the histogram equalization algorithm. In this paper, the proposed algorithm can make more pixels of the processed color images keep their color-saturations and reduce the gray-scale merging with Logarithmic histogram equalization algorithm. Test results with dim color images present a better effect of image enhancement.

scholarly journals Color Attenuation Prior (CAP) for Single Image Dehazing

2018 ◽  
Vol 7 (02) ◽  
pp. 23578-23584
Author(s):  
Miss. Anjana Navale ◽  
Prof. Namdev Sawant ◽  
Prof. Umaji Bagal
Keyword(s):  
State Of The Art ◽  
Depth Map ◽  
Depth Information ◽  
Single Image ◽  
Image Dehazing ◽  
Atmospheric Scattering ◽  
Haze Removal ◽  
Ill Posed ◽  
Single Input ◽  
Scene Depth

Single image haze removal has been a challenging problem due to its ill-posed nature. In this paper, we have used a simple but powerful color attenuation prior for haze removal from a single input hazy image. By creating a linear model for modeling the scene depth of the hazy image under this novel prior and learning the parameters of the model with a supervised learning method, the depth information can be well recovered. With the depth map of the hazy image, we can easily estimate the transmission and restore the scene radiance via the atmospheric scattering model, and thus effectively remove the haze from a single image. Experimental results show that the proposed approach outperforms state-of-the-art haze removal algorithms in terms of both efficiency and the dehazing effect.

scholarly journals Single Image Defogging Method Based on Image Patch Decomposition and Multi-Exposure Image Fusion

2021 ◽  
Vol 15 ◽  
Author(s):  
Qiuzhuo Liu ◽  
Yaqin Luo ◽  
Ke Li ◽  
Wenfeng Li ◽  
Yi Chai ◽  
...  
Keyword(s):  
Image Restoration ◽  
Physical Model ◽  
State Of The Art ◽  
The State ◽  
Depth Information ◽  
Single Image ◽  
Image Patch ◽  
Image Defogging ◽  
Image Patches ◽  
Scene Depth

Bad weather conditions (such as fog, haze) seriously affect the visual quality of images. According to the scene depth information, physical model-based methods are used to improve image visibility for further image restoration. However, the unstable acquisition of the scene depth information seriously affects the defogging performance of physical model-based methods. Additionally, most of image enhancement-based methods focus on the global adjustment of image contrast and saturation, and lack the local details for image restoration. So, this paper proposes a single image defogging method based on image patch decomposition and multi-exposure fusion. First, a single foggy image is processed by gamma correction to obtain a set of underexposed images. Then the saturation of the obtained underexposed and original images is enhanced. Next, each image in the multi-exposure image set (including the set of underexposed images and the original image) is decomposed into the base and detail layers by a guided filter. The base layers are first decomposed into image patches, and then the fusion weight maps of the image patches are constructed. For detail layers, the exposure features are first extracted from the luminance components of images, and then the extracted exposure features are evaluated by constructing gaussian functions. Finally, both base and detail layers are combined to obtain the defogged image. The proposed method is compared with the state-of-the-art methods. The comparative experimental results confirm the effectiveness of the proposed method and its superiority over the state-of-the-art methods.

Sign in / Sign up

Export Citation Format

Share Document