Dynamic Weights Equations for Converting Grayscale Image to RGB Image

Recently, there have been several automatic approaches to color grayscale images, which depend on the internal features of the grayscale images. There are several scales which are considered as a prominent key to extract the corresponding chromatic value of the gray level. In this aspect, colorizing methods that rely on automatic algorithms are still under investigation, especially after the development of neural networks used to recognize the features of images. This paper develops a new model to obtain a color image from an original grayscale image through the use of the Support Vector Machine to recognize the features of grayscale images which are extracted from two stages: the first stage is Haar Discrete Wavelets Transform used to configure the vector that combines with six of Statistical Measurements: (Mean, Variance, Skewness, Kurtosis, Energy and Standard Deviation) extracts from the grayscales image in the second stage. After the Support Vector Machine recognition has been done, the colorization process uses the result of Support Vector Machine to recover the color to greyscale images by using YCbCr color system then it converts the color to default color system (RGB) to be more clear. The proposed model will be able to move away from relying on the user to identify the source image which matches in color levels and it exceeds the network determinants of image types with similar color levels. In addition, Support Vector Machine is considered to be more reliable than neural networks in classification algorithms. The model performance is evaluated by using the Root Mean Squared Error (RMSE) in measuring the success of the assumed modal of matching the coloring (resulting) images and the original color images. So, a reality-related result has been obtained at a good rate for all the tested images. This model has proved to be successful in the process of recognizing the chromatic values of greyscale images then retrieving it. It takes less time complexity in trained data, and it isn’t complex in working.

Colour and greyscale (black and white) pictures look different to us, but it is not clear whether the difference in appearance is a consequence of the way our visual system uses colour signals or a by-product of our experience. In principle, colour images are qualitatively different from greyscale images because they make it possible to use different processing strategies. Colour signals provide important cues for segmenting the image into areas that represent different objects and for linking together areas that represent the same object. If this property of colour signals is exploited in visual processing we would expect colour stimuli to look different, as a class, from greyscale stimuli. We would also expect that adding colour signals to greyscale signals should change the way that those signals are processed. We have investigated these questions in behavioural and in physiological experiments. We find that male marmosets (all of which are dichromats) rapidly learn to distinguish between colour and greyscale copies of the same images. The discrimination transfers to new image pairs, to new colours and to image pairs in which the colour and greyscale images are spatially different. We find that, in a proportion of neurons recorded in the marmoset visual cortex, colour-shifts in opposite directions produce similar enhancements of the response to a luminance stimulus. We conclude that colour is, both behaviourally and physiologically, a distinctive property of images.

Tone-mapping-operators (TMOs) provide a useful means for converting high dynamic range (HDR) images to low dynamic range (LDR) images so that they can be viewed on standard displays, but this may influence the visual quality of experience (QoE) of the end-user. There is a need to understand the impact of TMOs to inform the choice of TMO algorithms for different displays, especially for small-screen-devices (SSDs) such as those used in mobile phones. This is important, as mobile devices are becoming the primary means of consuming multimedia contents. However, few studies have been undertaken to assess the impact of TMOs and viewing devices (especially SSDs) on the visual QoE of the user when using. In this chapter, we evaluate subjectively and objectively, the commonly used TMOs in different displays and resolutions for colour and grey-scale HDR images. Our results show that viewing devices have an influence on the TMOs performance, suggesting the need for a careful choice of TMO to enhance the viewing-QoE of the end-user. As expected, the higher resolution, the better HDR-image quality. Surprisingly, there was no significant difference between the Mean of Opinion Score (MOS) scores for colour and grey-scale images in SSDs. The device and TMOs affect QoE for colour and grey HDR-image equally. We found Shannon entropy (SE) to be a good objective measure of quality for colour and grey HDR images, suggesting that entropy may find use in automated HDR quality control assessment schemes, while; HDR-VDP-2 is a good objective measure for colour HDR image only.

Steganography is the art of the hidden data in such a way that it detection of hidden knowledge prevents. As the necessity of security and privacy increases, the need of the hiding secret data is ongoing. In this paper proposed an enhanced detection of the 1-2-4 LSB steganography and RSA cryptography in Gray Scale and Color images. For color images, we apply 1-2-4 LSB on component of the RGB, then encrypt information applying RSA technique. For Gray Images, we use LSB to then encrypt information and also detect edges of gray image. In the experimental outcomes, calculate PSNR and MSE. We calculate peak signal noise ratio for quality and brightness. This method makes sure that the information has been encrypted before hiding it into an input image. If in any case the cipher text got revealed from the input image, the middle person other than receiver can't access the information as it is in encrypted form.

This paper presents an efficient color image enhancement method for endoscopic images. The enhancement is achieved in two parts: image enhancement at gray level followed by a color reproduction. The captured RGB endoscopic images are first converted into three 2-D spectral images using a well-known method called FICE (Fuji Intelligent Color Enhancement). Then the image with the maximum entropy is selected as the base image to be used for color reproduction in the next stage. In color reproduction, the chrominance map of a source color image is added to the base image. This chrominance map is found by matching luminance and texture information between these two images based on neighborhood statistic method. The distance between luminance components of base gray image and source color images is calculated using 2-norm Euclidean distance. The proposed method highlights some of the tissue characteristics in the base endoscopic image which will enable better diagnosis. The performance of the scheme is compared with other related algorithms in terms of simulation speed, image quality, efficiency of color reproduction and distortion.

Due to advances in information technology, most of information can be kept electronically, consequently security of information of became a fundamental issues . Steganography is a technique of hiding information in digital media. A large number of commercial steganographic programs use the least significant bit embedding (LSB) as the method of choice for message hiding in 24-bit color images, 8-bit color images, and grayscale images. It is commonly believed that changes to the LSBs of colors cannot be detected due to noise that is always present in digital images. In this paper, it is proposed to use a LSB embedding algorithm to embed the payload bits of message image into cover image in spatial domain and detect LSB embedding in randomly scattered pixels in both 24-bit color images and 8bit grayscale or color images. By inspecting the differences in the number of regular and singular groups for the LSB and the "shifted LSB plane", it can reliably detect messages .By analyzing first order statistic like histogram analysis, entropy it can detect the presence of embedded data. The details of the system and results there of are presented in this paper. (4 pages)

Most existing methods for edge detection rely on contrast dependent thresholds. We show that a local measurement defined by the ratio of the smallest to the largest eigenvalue of the second moment matrix of filter kernels, can be used to separate smooth, low curvature curves and straight lines from noise, independent of contrast, in both color and gray level images. This is done without applying a threshold to the gradient magnitude. The edge images are defined as zero crossings in the gradient direction. The covariance matrix can easily be computed for both gray level images and color images. Further we show the potentiality of such a measure by integrating it with the Hough transform to extract long straight lines in noisy color images. The method is shown to successfully extract consistent line features from color images of a scene, captured under drastically different lightening conditions.

Imperceptibility is one of the most important aspects of the steganography method. Specifically, for steganography in images, embedded messages must not be felt by the human visual system. Humans have a higher sensitivity in color images compared to grayscale images, therefore the method that is still being tested on grayscale images needs to be retested on color images. Previous research has shown that the bit flipping method can increase imperceptibility to around 9dB in grayscale images. In this research the bit flipping method is tested on color images in the RGB format, the message capacity embedded in the image is 1 bit per pixel. The results of the imperceptibility test turned out to result in a more varied increase due to the greater number of layers in the color image. However, the bit flipping method is also proven to work well on color images, and even a maximum PSNR increase of more than 13dB. At the extraction stage, the message image can also be extracted perfectly, namely with the value NC = 1.

The application of confusion and diffusion processes on the three individual components of an RGB image is not secure and efficient, so this problem needs to be addressed. In this paper, a novel RGB image cipher is proposed using chaotic systems, 15-puzzle artificial intelligence problem and DNA computing. First of all the given color image is decomposed into its red, green and blue gray scale images. Then these gray scale images are concatenated to make a single gray scale image. This single gray scale image is further divided into different blocks. A block level permutation (BLP) is proposed on this gray scale image by using the 15-puzzle problem. A pixel level permutation is applied to further randomize the image pixels. This confused image is then DNA encoded. Afterwards, a diffusion process is applied on this DNA encoded image. Lastly this DNA diffused image is converted back into the decimal. Further, this single gray scale image is broken into three gray scale images. These three images are combined to get the final color cipher image. To create the plaintext sensitivity, SHA 256 hash function has been used. Both the simulation and a comprehensive security analyses suggest the robustness and the impregnability of the proposed scheme which in turn signals towards the real world applicability of the scheme.

Steganography is one of the methods used for secret communication. Steganography attempts to hide the existence of the information. The object used to hide the secret information is called as cover object. Images are the most popular cover objects used for steganography. Different techniques have to be used for color image steganography and grey scale image steganography since they are stored in different ways. Color image are normally stored with 24 bit depth and grey scale images are stored with 8 bit depth. Color images can hold large amount of secret information since they have three color components. Different color spaces namely RGB (Red Green Blue), HSV (Hue, Saturation, Value), YUV, YIQ, YCbCr (Luminance, hrominance) etc. are used to represent color images. Color image steganography can be done in any color space domain. In this paper color image steganography in RGB and YCbCr domain are compared. The secret information considered is grey scale image. Since RGB is the common method of representation, hiding secret information in this format is not secure.

Nowadays, the facilities that offers Internet in conjunction with the capabilities of the infrastructure of communication and information technologies, allowing that the multimedia data can be shared and transmitted in easy and efficient way. As consequence, the multimedia data can be copied or edited in an easy form, and this can be affecting its original content. In a context of digital images, this paper presents a tamper detection and localization algorithm in color images using secure block-based watermarking. This proposal is based on the frequency domain using the lifting wavelet transform, together with checksum criteria that make up the hash key codes, which they are hidden in the least significant bits of each low frequency component. The proposed method is evaluated in terms of imperceptibility and false positive, false negative and tamper detection rates, respectively. Watermark imperceptibility is measured using the peak signal to noise ratio and structural similarity index, respectively. The tamper detection ability has been tested on color and grayscale images using a wide variety of manipulations such as copy-paste, copymove, constant averaging, impulsive noise, and random manipulations. Comparative with the current state-of-the-art is provided.

In today's era of developed network, it is particularly important to ensure the security of information. Secret image sharing is a kind of secret information protection technology with loss tolerance by sharing a secret image to a number of shadow images. Most of the researches on secret image sharing focus on grayscale images, but the images we use in our daily life are mostly in color, so the research on the secret image sharing for color image is very meaningful and has great application value. The traditional color image sharing scheme is to share and recover R, G, and B color planes separately, which cause that the time of sharing and recovering one color image is at least triple that of sharing and recovering one grayscale image with the same size as the color image. In this paper, we propose an efficient and lossless secret image sharing scheme for color images. We concatenate the R, G, and B values of every color pixel into a long integer and share it by the polynomial-based secret image sharing, thus the sharing efficiency of color images is greatly improved by reducing the number of sharing operations to 1/3 of the traditional scheme. And we achieve lossless recovery by taking GF(16777259) as the finite field of polynomial operations. 16777259 is the smallest prime number greater than 2 24 - 1, so we intend to screen the random numbers to drop the invalid share values. Theoretical analyses and experiments are used to prove the effectiveness of the proposed scheme.

Introduction A digital image is a rectangular array of picture elements (pixels), arranged in m rows and n columns. The resolution of the image is m × n . Images can be categorized into bi-level, grayscale, and color images. A natural scene, such as a picture taken by a digital camera or obtained by using a scanner, is typically a continuous-tone image, where the colors vary continuously to the eye and there is a lot of noise in the picture. An artificial image, such as a graphical image, does not have the noise or blurring of a natural image. A cartoon-like image consists of uniform color in each area, but adjacent areas have different colors. The features in each type of image can be exploited to achieve a better compression. For example, for the bi-level image, each pixel is represented by one bit. A pixel has a high probability of being the same as its neighboring pixels, and thus RLE is suitable for compressing such image. The image can be scanned column by column or in zigzag. For the grayscale image, each pixel is represented by n bits, and a pixel tends to be similar to its immediate neighbors but may be not identical, thus RLE is not suitable. By representing the image using a Gray code that differs in only one bit for two consecutive integers, a grayscale image can be separated into n bi-level images, and each can be compressed by using RLE.

We propose a novel method to fuse true colour images with monochromatic non-visible range images that seeks to encode important structural information from monochromatic images efficiently but also preserve the natural appearance of the available true chromacity information. We utilise the β colour opponency channel of the lαβ colour as the domain to fuse information from the monochromatic input into the colour input by the way of robust grayscale fusion. This is followed by an effective gradient structure visualisation step that enhances the visibility of monochromatic information in the final colour fused image. Images fused using this method preserve their natural appearance and chromacity better than conventional methods while at the same time clearly encode structural information from the monochormatic input. This is demonstrated on a number of well-known true colour fusion examples and confirmed by the results of subjective trials on the data from several colour fusion scenarios.

Image quality is greatly affected by noise in various applications such as machine vision and computer vision. Noisy images lead to inaccurate results as well as segmentation and enhancement errors, making denoising vital to efficient image processing. Existing denoising methods do not work effectively on both grayscale and color images. Thus an effective denoising method that works well on both image types is proposed. The proposed two-stage method involves: (i) noisy pixel detection and (ii) noisy pixel restoration. In the first stage, the conglomerate method detects the noisy pixel position; in the second stage, the corrupted value of the noisy pixel is restored using a conglomerate method. The proposed method was used to process a variety of grayscale and color images. The result analysis of image denoising was done in two stages: in the first stage, results are carried out using large standard datasets; comparison of these results with those of five other denoising methods showed that the proposed conglomerate denoising method outperformed these existing ones. The second stage of result analysis considers the state-of-the-art methods in comparison with the proposed method. The proposed method outperforms the state-of-the-art methods for images affected with noise ranging from 10% to 90%.