scholarly journals Learning-Based Colorization of Grayscale Aerial Images Using Random Forest Regression

2018 ◽  
Vol 8 (8) ◽  
pp. 1269 ◽  
Author(s):  
Dae Seo ◽  
Yong Kim ◽  
Yang Eo ◽  
Wan Park
Keyword(s):  
Random Forest ◽  
Color Image ◽  
Color Space ◽  
Elevation Angle ◽  
Aerial Images ◽  
Aerial Image ◽  
Grayscale Image ◽  
Color Information ◽  
Image Colorization ◽  
Lab Color Space

Image colorization assigns colors to a grayscale image, which is an important yet difficult image-processing task encountered in various applications. In particular, grayscale aerial image colorization is a poorly posed problem that is affected by the sun elevation angle, seasons, sensor parameters, etc. Furthermore, since different colors may have the same intensity, it is difficult to solve this problem using traditional methods. This study proposes a novel method for the colorization of grayscale aerial images using random forest (RF) regression. The algorithm uses one grayscale image for input and one-color image for reference, both of which have similar seasonal features at the same location. The reference color image is then converted from the Red-Green-Blue (RGB) color space to the CIE L*a*b (Lab) color space in which the luminance is used to extract training pixels; this is done by performing change detection with the input grayscale image, and color information is used to establish color relationships. The proposed method directly establishes color relationships between features of the input grayscale image and color information of the reference color image based on the corresponding training pixels. The experimental results show that the proposed method outperforms several state-of-the-art algorithms in terms of both visual inspection and quantitative evaluation.

A Proposed Grayscale Face Image Colorization System using Particle Swarm Optimization

2017 ◽  
Vol 1 (1) ◽  
pp. 72-89 ◽  
Author(s):  
Abul Hasnat ◽  
Santanu Halder ◽  
Debotosh Bhattacharjee ◽  
Mita Nasipuri
Keyword(s):  
Real Time ◽  
Color Image ◽  
Turing Test ◽  
Face Image ◽  
Reference Image ◽  
Grayscale Image ◽  
Color Information ◽  
Image Colorization ◽  
Face Images ◽  
Real Time Applications

The proposed work is a novel grayscale face image colorization approach using a reference color face image. It takes a reference color image which presumably contains semantically similar color information for the query grayscale image and colorizes the grayscale face image with the help of the reference image. In this novel patch based colorization, the system searches a suitable patch on reference color image for each patch of grayscale image to colorize. Exhaustive patch search in reference color image takes much time resulting slow colorization process applicable for real time applications. So PSO is used to reduce the patch searching time for faster colorization process applicable in real time applications. The proposed method is successfully applied on 150 male and female face images of FRAV2D database. “Colorization Turing test” was conducted asking human subject to choose the image(close to the original color image) between colorized image using proposed algorithm and recent methods and in most of the cases colorized images using the proposed method got selected.

A Proposed Grayscale Face Image Colorization System Using Particle Swarm Optimization

2018 ◽  
pp. 886-904
Author(s):  
Abul Hasnat ◽  
Santanu Halder ◽  
Debotosh Bhattacharjee ◽  
Mita Nasipuri
Keyword(s):  
Real Time ◽  
Human Subject ◽  
Color Image ◽  
Turing Test ◽  
Face Image ◽  
Reference Image ◽  
Grayscale Image ◽  
Image Colorization ◽  
Face Images ◽  
Real Time Applications

The proposed work is a novel grayscale face image colorization approach using a reference color face image. It takes a reference color image which presumably contains semantically similar color information for the query grayscale image and colorizes the grayscale face image with the help of the reference image. In this novel patch based colorization, the system searches a suitable patch on reference color image for each patch of grayscale image to colorize. Exhaustive patch search in reference color image takes much time resulting slow colorization process applicable for real time applications. So PSO is used to reduce the patch searching time for faster colorization process applicable in real time applications. The proposed method is successfully applied on 150 male and female face images of FRAV2D database. “Colorization Turing test” was conducted asking human subject to choose the image (close to the original color image) between colorized image using proposed algorithm and recent methods and in most of the cases colorized images using the proposed method got selected.

Invertible Grayscale with Sparsity Enforcing Priors

2021 ◽  
Vol 17 (3) ◽  
pp. 1-17
Author(s):  
Yong Du ◽  
Yangyang Xu ◽  
Taizhong Ye ◽  
Qiang Wen ◽  
Chufeng Xiao ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Storage Capacity ◽  
Color Image ◽  
Grayscale Image ◽  
Storage Space ◽  
Color Information ◽  
Compression Process ◽  
The Arts ◽  
Residual Image

Color dimensionality reduction is believed as a non-invertible process, as re-colorization results in perceptually noticeable and unrecoverable distortion. In this article, we propose to convert a color image into a grayscale image that can fully recover its original colors, and more importantly, the encoded information is discriminative and sparse, which saves storage capacity. Particularly, we design an invertible deep neural network for color encoding and decoding purposes. This network learns to generate a residual image that encodes color information, and it is then combined with a base grayscale image for color recovering. In this way, the non-differentiable compression process (e.g., JPEG) of the base grayscale image can be integrated into the network in an end-to-end manner. To further reduce the size of the residual image, we present a specific layer to enhance Sparsity Enforcing Priors (SEP), thus leading to negligible storage space. The proposed method allows color embedding on a sparse residual image while keeping a high, 35dB PSNR on average. Extensive experiments demonstrate that the proposed method outperforms state-of-the-arts in terms of image quality and tolerability to compression.

scholarly journals Grayscale-based block scrambling image encryption using YCbCr color space for encryption-then-compression systems

2019 ◽  
Vol 8 ◽  
Author(s):  
Warit Sirichotedumrong ◽  
Hitoshi Kiya
Keyword(s):  
Image Encryption ◽  
Color Image ◽  
Color Space ◽  
Encryption Scheme ◽  
Brute Force ◽  
Jigsaw Puzzle ◽  
Color Information ◽  
Compression Performance ◽  
Ycbcr Color Space ◽  
Encrypted Images

AbstractA novel grayscale-based block scrambling image encryption scheme is presented not only to enhance security, but also to improve the compression performance for Encryption-then-Compression (EtC) systems with JPEG compression, which are used to securely transmit images through an untrusted channel provider. The proposed scheme enables the use of a smaller block size and a larger number of blocks than the color-based image encryption scheme. Images encrypted using the proposed scheme include less color information due to the use of grayscale images even when the original image has three color channels. These features enhance security against various attacks, such as jigsaw puzzle solver and brute-force attacks. Moreover, generating the grayscale-based images from a full-color image in YCbCr color space allows the use of color sub-sampling operation, which can provide the higher compression performance than the conventional grayscale-based encryption scheme, although the encrypted images have no color information. In an experiment, encrypted images were uploaded to and then downloaded from Twitter and Facebook, and the results demonstrated that the proposed scheme is effective for EtC systems and enhances the compression performance, while maintaining the security against brute-force and jigsaw puzzle solver attacks.

scholarly journals Automatic gray images colorization based on lab color space

2020 ◽  
Vol 18 (3) ◽  
pp. 1501
Author(s):  
Nidhal K. El Abbadi ◽  
Eman Saleem Razaq
Keyword(s):  
Color Image ◽  
Color Space ◽  
Accurate Method ◽  
Reference Image ◽  
The Public ◽  
Gray Image ◽  
Black And White ◽  
Manual Intervention ◽  
Lab Color Space ◽  
Fully Automatic

<p>The colorization aim to transform a black and white image to a color image. This is a very hard  issue and usually requiring manual intervention by the user to produce high-quality images free of artifact. The public problem of inserting gradients color to a gray image has no accurate method. The proposed method is fully automatic method. We suggested to use reference color image to help transfer colors from reference image to gray image.  The reference image converted to  Lab color space, while the gray scale image normalized according to the lightness channel L. the gray image concatenate with both a, and b channels before converting to RGB image. The results were promised compared with other methods.</p>

scholarly journals Comparative Analysis of Color Video Enhancment Techniques

2013 ◽  
Vol 11 (4) ◽  
pp. 2484-2489
Author(s):  
Rajeev Sunakara ◽  
P.Ravi Sankar
Keyword(s):  
Image Enhancement ◽  
Adaptive Filter ◽  
Color Image ◽  
Color Space ◽  
Color Information ◽  
Color Space Conversion ◽  
Color Image Enhancement ◽  
Color Restoration ◽  
Color Video ◽  
Enhancement Algorithm

Contrast enhancement has an important role in image processing applications. This paper presents a color enhancement algorithm based on adaptive filter technique. First, the proposed method is divided into three major parts: obtain luminance image and backdrop image, adaptive modification and color restoration. different traditional color image enhancement algorithms, the adaptive filter in the algorithm takes color information into consideration. The algorithm finds the significance of color information in color image enhancement and utilizes color space conversion to obtain a much better visibility. In the practical results, the proposed method reproduces better enhancement and reduce the halo distortion compared with the bilateral  methods.

A Fast Algorithm for Lip Contour and Feature Extraction

2012 ◽  
Vol 182-183 ◽  
pp. 2040-2044 ◽  
Author(s):  
Xi Bin Jia ◽  
Ke Wei Zhang
Keyword(s):  
Color Space ◽  
Visual Speech ◽  
Fast Method ◽  
Color Information ◽  
Adaboost Algorithm ◽  
Face Shape ◽  
The Face ◽  
Lab Color Space ◽  
Speech Features ◽  
Feature Values

The paper proposes a fast method that uses the lips color information in the Lab color space and the pre-knowledge of geometric characteristics around lip areas to extract the lip contour and visual speech features from color images or video sequences with front talking faces. In our method, the Adaboost algorithm is utilized to realize the face detection. Then, the mouth area is segmented based on the face shape attribution. According to the relative position of the trough and crest of the histogram, we can get an adaptive threshold. The A-component in the Lab color space was used to extract the outer lip and the L-component is used to extract the inner lip. From the contour image, we obtain the feature by searching twice the points of the contour. The experimental results show that obtained visual feature values in our approach are approximate to that with AAM algorithm but with less computation complexity.

scholarly journals RESEARCH OF IMAGES FILTRATION METHODS IN COMPUTER SYSTEMS

2021 ◽  
Vol 5 (1) ◽  
pp. 93-99
Author(s):  
Oleksandr Tymochko ◽  
Volodymyr Larin ◽  
Maksym Kolmykov ◽  
Oleksander Timochko ◽  
Vladislava Pavlenko
Keyword(s):  
Color Image ◽  
Image Representation ◽  
Color Space ◽  
Visual Image ◽  
Color Model ◽  
Color Information ◽  
Color Formation ◽  
Rgb Color Space ◽  
Human Eyes ◽  
Three Components

It is known that human eyes are less sensitive to color, than to their brightness. In the RGB color space, all three components are considered equally important, and they are usually stored with the same resolution. However, you can display a color image more efficiently, separating the brightness from color information and presenting it with a higher resolution than color. RGB space is well suited for computer graphics, because it uses these three components for color formation. However, RGB space is not very effective when it comes to real images. The fact is that to save the color of an image, you need to know and store all three components of the RGB, and if one of them is missing, it will greatly distort the visual image representation. Also, when processing images in RGB space, it is not always convenient to perform any pixel conversion, because, in this case, it will be necessary to list all three values of the RGB component and write back. This greatly reduces the performance of various image processing algorithms. For these and other reasons, many video standards use brightness and two signals that carry information about the red and blue components of the signal, as a color model other than RGB. The most famous among such spaces is YCbCr.

scholarly journals A Modeling Approach on the Correction Model of the Chromatic Aberration of Scanned Wood Grain Images

Coatings ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 79
Author(s):  
Jingjing Mao ◽  
Zhihui Wu ◽  
Xinhao Feng
Keyword(s):  
Color Change ◽  
Color Image ◽  
Color Space ◽  
Chromatic Aberration ◽  
Practical Significance ◽  
Color Information ◽  
Correction Model ◽  
Before And After ◽  
Scanned Images ◽  
Wood Grain

There always exists subjective and objective color differences between digital wood grain and real wood grain, making it difficult to replicate the color of natural timber. Therefore, we described a novel method of correcting the chromatic aberration of scanned wood grain to maximally restore the objective color information of the real wood grain. A point-to-point correction model of chromatic aberration between the scanned wood grain and the measured wood grain was established based on Circle 1 by adjusting the three channels (sR, sG, and sB) of the scanned images. A conversion of the color space was conducted using the mutual conversion formulas. The color change of the scanned images before and after the correction was evaluated through the L* a* b* color-mode-based ΔE* and the lαβ color-model-based CIQI (Color Image Quality Index) and CQE (Color Quality Enhancement). The experimental results showed that the chromatic aberration ΔE* between the scanned wood grain and the measured wood grain decreased and the colorfulness index CIQI of the scanned wood grain increased for most wood specimens after the correction. The values of ΔE* of the twenty kinds of wood specimens decreased by an average of 3.1 in Circle 1 and 2.3 in Circle 2, thus the correction model established based on Circle 1 was effective. The color of the scanned wood grain was more consistent with that of the originals after the correction, which would provide a more accurate color information for the reproductions of wood grain and had an important practical significance.

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