scholarly journals Restoration method of sootiness mural images based on dark channel prior and Retinex by bilateral filter

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Ning Cao ◽  
Shuqiang Lyu ◽  
Miaole Hou ◽  
Wanfu Wang ◽  
Zhenhua Gao ◽  
...  
Keyword(s):  
Near Infrared ◽  
Environmental Changes ◽  
Color Image ◽  
Visual Quality ◽  
Bilateral Filter ◽  
Dark Channel Prior ◽  
Infrared Band ◽  
Dark Channel ◽  
Restoration Method ◽  
Pseudo Color

AbstractEnvironmental changes and human activities can cause serious degradation of murals, where sootiness is one of the most common problems of ancient Chinese indoor murals. In order to improve the visual quality of the murals, a restoration method is proposed for sootiness murals based on dark channel prior and Retinex by bilateral filter using hyperspectral imaging technology. First, radiometric correction and denoising through band clipping and minimum noise fraction rotation forward and inverse transform were applied to the hyperspectral data of the sootiness mural to produce its denoised reflectance image. Second, a near-infrared band was selected from the reflectance image and combined with the green and blue visible bands to produce a pseudo color image for the subsequent sootiness removal processing. The near-infrared band is selected because it is better penetrating the sootiness layer to a certain extent comparing to other bands. Third, the sootiness covered on the pseudo color image was preliminarily removed by using the method of dark channel prior and by adjusting the brightness of the image. Finally, the Retinex by bilateral filter was performed on the image to get the final restored image, where the sootiness was removed. The results show that the images restored by the proposed method are superior in variance, average gradient, information entropy and gray scale contrast comparing to the results from the traditional methods of homomorphic filtering and Gaussian stretching. The results also show the highest score in comprehensive evaluation of edges, hue and structure; thus, the method proposed can support more potential studies or sootiness removal in real mural paintings with more detailed information. The method proposed shows strong evidence that it can effectively reduce the influence of sootiness on the moral images with more details that can reveal the original appearance of the mural and improve its visual quality.

scholarly journals Restoration Method of Sootiness Mural Images Based on Dark Channel Prior and Retinex by Bilateral Filter

2020 ◽  
Author(s):  
Ning Cao ◽  
Shuqiang Lyu ◽  
Miaole Hou ◽  
Wanfu Wang ◽  
Zhenhua Gao ◽  
...  
Keyword(s):  
Near Infrared ◽  
Environmental Changes ◽  
Color Image ◽  
Visual Quality ◽  
Bilateral Filter ◽  
Dark Channel Prior ◽  
Infrared Band ◽  
Dark Channel ◽  
Restoration Method ◽  
Pseudo Color

Abstract Environmental changes and human activities can cause serious degradation of murals, where sootiness is one of the most common problems of ancient Chinese indoor murals. In order to improve the visual quality of the murals, a restoration method is proposed for sootiness murals based on dark channel prior and Retinex by bilateral filter using hyperspectral imaging technology. First, radiometric correction and denoising through band clipping and minimum noise fraction rotation forward and inverse transform were applied to the hyperspectral data of the sootiness mural to produce its denoised reflectance image. Second, a near-infrared band was selected from the reflectance image and combined with the green and blue visible bands to synthesize a pseudo color image for the subsequent sootiness removal processing. The near-infrared band is selected because it is better penetrating the sootiness layer to a certain extent comparing to other bands. Third, the sootiness covered on the pseudo color image was preliminarily removed by using the method of dark channel prior and by adjusting the brightness of the image. Finally, the Retinex by bilateral filter was performed on the image to get the final restored image, where the sootiness was removed. The results show that the proposed method can effectively reduce the influence of sootiness on the mural image and improve its visual quality. It can also be used to reveal the original appearance of the mural to reasonable extent.

Camouflage Target Identification Based on Hyperspectral Imaging System

2011 ◽  
Vol 135-136 ◽  
pp. 341-346
Author(s):  
Na Ding ◽  
Jiao Bo Gao ◽  
Jun Wang
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging System ◽  
Target Identification ◽  
Spectral Characteristics ◽  
Tunable Filter ◽  
Fusion Algorithm ◽  
Infrared Band ◽  
Target Segmentation ◽  
Pseudo Color

A novel system of implementing target identification with hyperspectral imaging system based on acousto-optic tunable filter (AOTF) was proposed. The system consists of lens, AOTF, AOTF driver, CCD and image collection installation. Owing to the high spatial and spectral resolution, the system can operate in the spectral range from visible light to near infrared band. An experiment of detecting and recognizing of two different kinds of camouflage armets from background was presented. When the characteristic spectral wave bands are 680nm and 750nm, the two camouflage armets exhibit different spectral characteristic. The target camouflage armets in the hyperspectral images are distinct from background and the contrast of armets and background is increased. The image fusion, target segmentation and pick-up of those images with especial spectral characteristics were realized by the Hyperspectral Imaging System. The 600nm, 680nm, and 750nm images were processed by the Pseudo color fusion algorithm, thus the camouflage armets are more easily observed by naked eyes. Experimental results confirm that AOTF hyperspectral imaging system can acquire image of high contrast, and has the ability of detecting and identification camouflage objects.

A Modified Haze Removal Algorithm Using Dark Channel Prior

2012 ◽  
Vol 457-458 ◽  
pp. 1397-1402
Author(s):  
Xiao Tian Wu ◽  
Xing Hao Ding ◽  
Quan Xiao
Keyword(s):  
Adaptive Algorithm ◽  
Bilateral Filter ◽  
Input Image ◽  
Dark Channel Prior ◽  
Edge Preserving ◽  
Haze Removal ◽  
Dark Channel ◽  
Color Shift ◽  
Single Input ◽  
Channel Image

In this paper, we propose a new algorithm to remove haze from a single input image. Based on the Dark Channel Prior proposed by He [1], we exploit the Gauss Bilateral Filter and the min operation to obtain an edge-preserving dark channel image, which is non-iterative, requires less time. We further utilize this dark channel image to extract the estimation of medium transmission, and finally recover a haze-free image from that. Furthermore, we use a self-adaptive algorithm to set the haze parameters to solve the color shift problem for large sky region. Experiments demonstrate our algorithm can effectively remove haze from a foggy image while keep edges sharp.

scholarly journals Single Color Image De-Hazing Using Dark Channel Prior

2021 ◽  
pp. 42-49
Author(s):  
M. Suganthi ◽  
M. Surya Muthukumar
Keyword(s):  
Real World ◽  
Color Image ◽  
Weather Condition ◽  
Structural Features ◽  
Input Image ◽  
Outdoor Environment ◽  
Dark Channel Prior ◽  
Research Focus ◽  
Image Dehazing ◽  
Dark Channel

In real world scenario due to bad weather conditions the presence of fog and haze, the particles in the outdoor environment or atmosphere (e.g., droplets, smoke, sand, snow, mist, volcanic ash, liquid dust or solid dust) greatly reduces the visibility of the scene. As a consequence, the clarity of an image would be seriously degraded, which may decrease the performance of many image processing applications. Image Dehazing methods try to alleviate these problems by estimating a haze free version of the given hazy image. Traditionally the task of image dehazing can be processed as recovering the scene radiance from a noisy hazy image by estimating the atmospheric light and transmission properties. In those kinds of techniques, it additionally needs some more information regarding the image such as scene depth, weather condition parameters and so on. But this is not suitable for real world scenario. This research focus on proposing an approach to fully capture the intrinsic attributes of a hazy image and improves the performance of dehazing. Dark Channel Prior plays vital role in dehazing process. Hence this research focus on recovering dehaze version of the input image by CNN. So that all methods are comes under the categories image enhancement, image fusion image restoration based on statistical and structural features of the hazed image.

scholarly journals Dark Channel Prior Based Image Dehazing using Different Transmission Map Refinement Methods

2019 ◽  
Vol 8 (12) ◽  
pp. 3015-3020
Keyword(s):  
Image Reconstruction ◽  
Image Quality ◽  
Ground Truth ◽  
Bilateral Filter ◽  
Dark Channel Prior ◽  
Blocking Artifacts ◽  
Image Dehazing ◽  
Data Set ◽  
Dark Channel ◽  
Atmospheric Light

Due to existence of haze, the image quality is degraded in the environment. Removal of haze is called dehazing. To dehaze an image Dark Channel Prior is recommended. Dark Channel Prior is an observation, that an image has few pixels whose intensity value is very small or near to zero in most non-sky patches. Such pixels are referred to as dark pixels. Dehazing through Dark Channel Prior is accomplished using four major steps. The steps include estimating atmospheric light, estimating transmission map, refinement of transmission map and image reconstruction. Incorrect estimation of transmission map may lead to some problems. These problems include false textures and blocking artifacts. Many methods are developed to further sharpen transmission map. Here transmission map is refined using soft matting, guided filter and bilateral filter. The comparison of dehazing methods has become difficult due to scarce availability of ground truth images .So we used I-HAZE, a new data set containing 35 picture pairs of hazy pictures and their respective ground truth pictures. A significant benefit of I-HAZE data set is that it allows us to compare different refinement methods used for dehazing with SSIM, PSNR and RMSE which are used for the measurement of finally obtained reconstructed image quality after the removal of haze.

Research on Images Restoration Method under the Foggy Environment

2014 ◽  
Vol 1070-1072 ◽  
pp. 2037-2040
Author(s):  
Hui Liu ◽  
Xue Bin Liu
Keyword(s):  
Histogram Equalization ◽  
Paper Analysis ◽  
Dark Channel Prior ◽  
Atmospheric Scattering ◽  
Adaptive Histogram Equalization ◽  
Dark Channel ◽  
Histogram Similarity ◽  
Restoration Method ◽  
Degraded Images

Because of the atmospheric scattering phenomenon, weather atmospheric degraded images captured in foggy environment have poor contrast and visibility, it has seriously affected the quality of the images. So this paper analysis and find something different between the dark channel prior and the interpolating self-adaptive histogram equalization method based on physical and non-physical model. And using the histogram similarity evaluation is evaluated on them. Finally, further discussion are indicated on techniques challenges and future development.

scholarly journals Image Dehazing Using Near-Infrared Information Based on Dark Channel Prior

2021 ◽  
Vol 187 ◽  
pp. 18-23
Author(s):  
Zhen Hua ◽  
Yuanjuan Ding ◽  
Jinjiang Li
Keyword(s):  
Near Infrared ◽  
Dark Channel Prior ◽  
Image Dehazing ◽  
Dark Channel
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