scholarly journals Histogram Adjustment of Images for Improving Photogrammetric Reconstruction

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4654
Author(s):  
Piotr Łabędź ◽  
Krzysztof Skabek ◽  
Paweł Ozimek ◽  
Mateusz Nytko
Keyword(s):  
Image Enhancement ◽  
High Efficiency ◽  
Color Image ◽  
Histogram Equalization ◽  
Reconstruction Accuracy ◽  
Reconstruction Process ◽  
Reference Models ◽  
Color Models ◽  
Selection Of

The accuracy of photogrammetric reconstruction depends largely on the acquisition conditions and on the quality of input photographs. This paper proposes methods of improving raster images that increase photogrammetric reconstruction accuracy. These methods are based on modifying color image histograms. Special emphasis was placed on the selection of channels of the RGB and CIE L*a*b* color models for further improvement of the reconstruction process. A methodology was proposed for assessing the quality of reconstruction based on premade reference models using positional statistics. The analysis of the influence of image enhancement on reconstruction was carried out for various types of objects. The proposed methods can significantly improve the quality of reconstruction. The superiority of methods based on the luminance channel of the L*a*b* model was demonstrated. Our studies indicated high efficiency of the histogram equalization method (HE), although these results were not highly distinctive for all performed tests.

Analysis of color Image Enhancement Using DWT, Wavelet Shrinkageand FHE Methods

2017 ◽  
Vol 7 (8) ◽  
pp. 56
Author(s):  
Ashish Dwivedi ◽  
Nirupma Tiwari
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Human Perception ◽  
Histogram Equalization ◽  
Wavelet Shrinkage ◽  
Visual Appearance ◽  
Enhancement Method ◽  
Fuzzy Histogram ◽  
Rgb Image

Image enhancement (IE) is very important in the field where visual appearance of an image is the main. Image enhancement is the process of improving the image in such a way that the resulting or output image is more suitable than the original image for specific task. With the help of image enhancement process the quality of image can be improved to get good quality images so that they can be clear for human perception or for the further analysis done by machines.Image enhancement method enhances the quality, visual appearance, improves clarity of images, removes blurring and noise, increases contrast and reveals details. The aim of this paper is to study and determine limitations of the existing IE techniques. This paper will provide an overview of different IE techniques commonly used. We Applied DWT on original RGB image then we applied FHE (Fuzzy Histogram Equalization) after DWT we have done the wavelet shrinkage on Three bands (LH, HL, HH). After that we fuse the shrinkage image and FHE image together and we get the enhance image.

scholarly journals Hue-Preserving Saturation Improvement in RGB Color Cube

2021 ◽  
Vol 7 (8) ◽  
pp. 150
Author(s):  
Kohei Inoue ◽  
Minyao Jiang ◽  
Kenji Hara
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Color Space ◽  
Histogram Equalization ◽  
Experimental Results ◽  
Color Image Enhancement ◽  
Histogram Specification ◽  
Target Intensity ◽  
Rgb Color Space ◽  
Intensity Image

This paper proposes a method for improving saturation in the context of hue-preserving color image enhancement. The proposed method handles colors in an RGB color space, which has the form of a cube, and enhances the contrast of a given image by histogram manipulation, such as histogram equalization and histogram specification, of the intensity image. Then, the color corresponding to a target intensity is determined in a hue-preserving manner, where a gamut problem should be taken into account. We first project any color onto a surface in the RGB color space, which bisects the RGB color cube, to increase the saturation without a gamut problem. Then, we adjust the intensity of the saturation-enhanced color to the target intensity given by the histogram manipulation. The experimental results demonstrate that the proposed method achieves higher saturation than that given by related methods for hue-preserving color image enhancement.

scholarly journals Enhancement of Satellite Imagesusing Image Sharpening Technique

2019 ◽  
Vol 8 (6S2) ◽  
pp. 351-354
Keyword(s):  
Image Processing ◽  
Color Image ◽  
Histogram Equalization ◽  
Grayscale Image ◽  
Original Image ◽  
High Quality ◽  
Wide Range ◽  
Equalization Technique ◽  
The Difference

In many image processing applications, a wide range of image enhancement techniques are being proposed. Many of these techniques demanda lot of critical and advance steps, but the resultingimage perception is not satisfactory. This paper proposes a novel sharpening method which is being experimented with additional steps. In the first step, the color image is transformed into grayscale image, then edge detection process is applied using Laplacian technique. Then deduct this image from the original image. The resulting image is as expected; After performing the enhancement process,the high quality of the image can be indicated using the Tenengrad criterion. The resulting image manifested the difference in certain areas, the dimension and the depth as well. Histogram equalization technique can also be applied to change the images color.

An Image Enhancement Method Based on the Coal Mine Monitoring System

2012 ◽  
Vol 468-471 ◽  
pp. 204-207
Author(s):  
Zhen Chong Wang ◽  
Yan Qin Zhao
Keyword(s):  
Image Enhancement ◽  
Coal Mine ◽  
Impulse Noise ◽  
Histogram Equalization ◽  
Video Monitor ◽  
Low Contrast ◽  
Adaptive Histogram Equalization ◽  
Enhancement Method ◽  
Special Environment

For the low illumination and low contrast in the coal mine, images captured from the video monitor system sometimes are not so clear to help the related personal monitoring the production and safety of the mine. According to the special environment of coal mine, an image enhancement method was presented. In this method the impulse noise which is the mainly noise in the coal mine was first reduced with median filtering, then the low contrast and illumination was greatly improved with the improved adaptive histogram equalization. Experiments show that this method can improve the quality of images underground effectively.

scholarly journals Digital Image Enhancement in Matlab: An Overview on Histogram Equalization and Specification

2018 ◽  
Author(s):  
Rezoana Bente Arif ◽  
Mohammad Mahmudur Rahman Khan ◽  
Md. Abu Bakr Siddique
Keyword(s):  
Image Enhancement ◽  
Digital Image ◽  
Color Image ◽  
Histogram Equalization ◽  
Grayscale Image ◽  
Histogram Specification ◽  
Digital Image Enhancement

This paper has two major parts. In the first part histogram equalization for the image enhancement was implemented without using the built-in function in MATLAB. Here, at first, a color image of a rat was chosen and the image was transformed into a grayscale image. After this conversion, histogram equalization was implemented on the grayscale image. Later on, in the same image for each RGB channel, histogram equalization was implemented to observe the effect of histogram equalization on each channel. In the end, the histogram equalization was implemented to this specific color image of a rat. In the second part, for the grayscale image in part 1, the desired histogram of another colored image of a rat was introduced and histogram specification was implemented on the original colored image.

scholarly journals Color Systems and Color Image Enhancement Methods

1970 ◽  
Vol 10 (2) ◽  
pp. 97-110
Author(s):  
Akira Taguchi
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Color Space ◽  
Histogram Equalization ◽  
Color Image Processing ◽  
Saturation Intensity ◽  
Color Image Enhancement ◽  
Color System ◽  
Hsi Color Space ◽  
Rgb Color Space

There are many color systems. Some systems are correspond to the human visual system, such as the Munsell color system. Other systems are formulated to ease data processing in machines, such as RGB color space. At first, Munsell color system is introduced in this paper. Next, RGB color system and hue-saturation-intensity (HSI) color system which is derived from RGB color systems are reviewed. HSI color system is important, because HSI color system is closely related to Munsell color system. We introduce the advantage and drawbacks of the conventional HSI color space. Furthermore, the improved HSI color system is introduced. The second half of this paper, we introduce a lot of color image enhancement methods based on the histogram equalization or the differential histogram equalization. Since hue preserving is necessary for color image processing, intensity processing methods by using both intensity and saturation in HSI color space are reviewed. Finally, hue preserving color image enhancement methods in RGB color system are explained.

Combined Gray Level Transformation Technique for Low Light Color Image Enhancement

2021 ◽  
Vol 18 (4) ◽  
pp. 1221-1226
Author(s):  
Durai Pandurangan ◽  
R. Saravana Kumar ◽  
Lukas Gebremariam ◽  
L. Arulmurugan ◽  
S. Tamilselvan
Keyword(s):  
Color Image ◽  
Computation Time ◽  
Histogram Equalization ◽  
Light Illumination ◽  
Gray Level ◽  
Low Light ◽  
Transformation Technique ◽  
Light Color ◽  
Smart Traffic

Insufficient and poor lightning conditions affect the quality of videos and images captured by the camcorders. The low quality images decrease the performances of computer vision systems in smart traffic, video surveillance, and other imaging systems applications. In this paper, combined gray level transformation technique is proposed to enhance the less quality of illuminated images. This technique is composed of log transformation, power law transformation and adaptive histogram equalization process to improve the low light illumination image estimated using HIS color model. Finally, the enhanced illumination image is blended with original reflectance image to get enhanced color image. This paper shows that the proposed algorithm on various weakly illuminated images is enhanced better and has taken reduced computation time than previous image processing techniques.

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