Color Image Enhancement Research Papers (original) (raw)

In this paper a quaternion approach of enhancement method is proposed in which color in the image is
considered as a single entity. This new method is referred as the alpha-rooting method of color image
enhancement by the two-dimensional quaternion discrete Fourier transform (2-D QDFT) followed by a
spatial transformation. The results of the proposed color image enhancement method are compared with its
counterpart channel-by-channel enhancement algorithm by the 2-D DFT. The image enhancements are
quantified to the enhancement measure that is based on visual perception referred as the color
enhancement measure estimation (CEME). The preliminary experiment results show that the quaternion
approach of image enhancement is an effective color image enhancement technique.

Abstract. In this contribution we present experiments on color image enhancement for several dierent non-linear lters which originally were de ned for gray-level images. We disturb sample images by dierent types of noise and measure performance of the lters. We provide signal-to-noise measurements as well as perceived color dierence in E as dened by the CIE. All images and test programs are provided online on the internet so that experiments can be validated by arbitrary users on any image data.

- by Henryk Palus
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- Performance, Color Image Enhancement, Color Image, Noise Measurement

Different kinds of Images captured are an important medium to represent meaningful information. It can be problematic for artificial intelligence, computer vision techniques and detection algorithms to extract valuable information from those images with poor lighting. In this paper, a study of low illumination-based low-light night image enhancement techniques are presented which work on reflectance, degradation, unsatisfactory lightings, noise, limited range visibility, low contrast, color variations, illumination, color distortion, and quality is reduced. Improving the images in low light conditions is a prerequisite in many fields, such as surveillance systems, road safety and inland waterway transport, object tracking, scientific research, the detection system, the counting system and the navigation system. Low-illumination or night image enhancement algorithms can advance the visual quality of low-light images and these images can be used in many practical application's artificial intelligence and computer vision techniques. The methods used for enhancement of low illumination must perform, preserving details, contrast improvement, color correction, noise reduction, image enhancement, restoration, etc.

The underwater digital images generally suffer from blur, low contrast, non-uniform lighting, and diminished color. This research paper proposed a preprocessing technique based on image to improve the quality of underwater digital images. The mixed Contrast Limited Adaptive Histogram Equilization (CLAHE) has actually neglected the utilization of L*A*B color image space to improve the image in an effective way. Also the uneven illumination problem is also ignored by many researchers. To conquer the problems of on hand technique a brand new L*A*B color image space as well as CLAHE based digital image enhancement technique is proposed in this paper. To conquer the problem of uneven illumination in the resultant image of the CLAHE image output has been further removed by utilizing the smoothing process of image gradient. The main objective of the planned algorithm is to enhance the accuracy of the underwater digital image enhancement methods/techniques. Various types of digital images will be considered for experimental point of view to estimate the efficacy of the image enhancement methods or techniques. Also, various types of image top-quality metrics have been utilized in order to check the significant improvement of the recommended technique over the offered techniques. The significant improvements have shown in the comparative analysis of the proposed algorithm over the available mixed CLAHE. Keywords Preprocessing Underwater Image, CLAHE, L*A*B* Color Image Space, Image Gradient, Image Enhancement, Image Smoothing

- by Ramandeep Kaur
- •
- Digital Image Processing, Color Image Enhancement

Enhancement of color images is a very crucial and necessary task in the era of image processing. Image enhancement also plays a very essential role in the field of space science, medical imaging etc. improved medical images are more appropriate for analysis and correct diagnosis. This paper represents several color space techniques (RGB, YIQ, YCbCr, HSV etc.) to represents colored digital images. Also performs color space transformation on color images and simulates on MATLAB tool and compare result of several different color spaces. This paper transforms color space of the images from RGB to HSV, YIQ, YCbCr respectively. Hence it obtains a visually enhanced edition of the noised image.

- by Agnes Jaya Kanishta
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- Image Processing, Quantization, Contrast, MPEG
- by JP INFOTECH PROJECTS
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- Artificial Intelligence, Image Processing, Machine Learning, HIV/AIDS
- by Hari Babu
- •
- Image Processing, Quantization, Contrast, MPEG

Color image segmentation is a very emerging research topic in the area of color image analysis and pattern recognition. Many state-of-the-art algorithms have been developed for this purpose. But, often the segmentation results of these algorithms seem to be suffering from miss-classifications and over-segmentation. The reasons behind these are the degradation of image quality during the acquisition, transmission and color space conversion. So, here arises the need of an efficient image enhancement technique which can remove the redundant pixels or noises from the color image before proceeding for final segmentation. In this paper, an effort has been made to study and analyze different image enhancement techniques and thereby finding out the better one for color image segmentation. Also, this comparative study is done on two well-known color spaces HSV and LAB separately to find out which color space supports segmentation task more efficiently with respect to those enhancement techniques.

- by DIBYA BORA
- •
- Image Processing, Medical Image Processing, Digital Signal Processing, Image Analysis

— Skin cancer is one of the most prevalent types of cancer in our world. Diagnosis of skin cancer needs specialized equipment, doctors and continuous monitoring. Patients living in remote areas normally cannot access such facilities. To overcome these barriers of access, Computer Aided Diagnostics, an emerging field in computer science, often called telemedicine, is being considered a promising approach. Image processing for Computer Aided Diagnostics has three key steps, i.e. Segmentation, Feature Extraction and Classification. In this research, preprocessing and hair artifact removal experiment was performed on dermatoscope images by using Morphological and Gabor wavelet-based techniques. It has been found that, in some cases, wavelet transformations provide better results as compared to other techniques like gel, water bubbles and dark hair around the surface affected by cancer, i.e. these artifacts are removed with less effort. Experiments also showed that images with Blue channel from RGB are better as compared to other grayscale conversion techniques.

- by Uzma Jamil
- •
- Computer Science, Biomedical Engineering, Image Processing, Color Image Enhancement

A novel quaternion color representation tool is proposed to the images and videos efficiently. In this work, we consider a full model for representation and processing color images in the quaternion algebra. Color images are presented in the threefold complex plane where each color component is described by a complex image. Our preliminary experimental results show significant performance improvements of the proposed approach over other well-known color image processing techniques. Moreover, we have shown how a particular image enhancement of the framework leads to excellent color enhancement (better than other algorithms tested). In the framework of the proposed model, many other color processing algorithms, including filtration and restoration, can be expressed.

- by Sarif Naik
- •
- Color Image Enhancement

Color in an image is resolved to 3 or 4 color components and 2-Dimages of these components are stored in separate channels. Most of the color image enhancement algorithms are applied channel-by-channel on each image. But such a system of color image processing is not processing the original color. When a color image is represented as a quaternion image, processing is done in original colors. This paper proposes an implementation of the quaternion approach of enhancement algorithm for enhancing color images and is referred as the modified alpha-rooting by the two-dimensional quaternion discrete Fourier transform (2-D QDFT). Enhancement results of this proposed method are compared with the channel-by-channel image enhancement by the 2-D DFT. Enhancements in color images are quantitatively measured by the color enhancement measure estimation (CEME), which allows for selecting optimum parameters for processing by thegenetic algorithm. Enhancement of color images by the quaternion based method allows for obtaining images which are closer to the genuine representation of the real original color.

A new approach for tuning the parameters of MultiScale Retinex
(MSR) based color image enhancement algorithm using a popular
optimization method, namely, Particle Swarm Optimization (PSO) is
presented in this paper. The image enhancement using MSR scheme
heavily depends on parameters such as Gaussian surround space
constant, number of scales, gain and offset etc. Selection of these
parameters, empirically and its application to MSR scheme to produce
inevitable results are the major blemishes. The method presented here
results in huge savings of computation time as well as improvement
in the visual quality of an image, since the PSO exploited maximizes
the MSR parameters. The objective of PSO is to validate the visual
quality of the enhanced image iteratively using an effective objective
criterion based on entropy and edge information of an image. The
PSO method of parameter optimization of MSR scheme achieves a
very good quality of reconstructed images, far better than that possible
with the other existing methods. Finally, the quality of the enhanced
color images obtained by the proposed method are evaluated using
novel metric, namely, Wavelet Energy (WE). The experimental results
presented show that color images enhanced using the proposed scheme
are clearer, more vivid and efficient.

We present techniques for the processing of color, high-dynamic luminance images of a type aiming for objectivity and also of a type aiming for aesthetic improvement. In the first case we start with camera raw data, propose a variant white balance, darken very light spots and lighten very dark spots. In the second case we use color spaces of the type hue-saturation-luminance; we propose a hue processing method inspired in the Bezold-Brucke effect as well as a luminance-dependant displacement of color saturation.

- by Alfredo Restrepo
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- Engineering, Computer Science, High Dynamic Range, HSV
- by Mihaela Gordan and +1
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- Image Processing, Fuzzy Sets, Color Image Enhancement, ekstraksi ciri Discrete Cosine Transform (DCT)

Image enhancement is one of the most significant techniques in digital image processing. This paper introduces two image enhancement methods, Weighted of Local and Bidirectional Smooth Histogram Stretching (WLBSHS) and Local then Bidirectional Smooth Histogram Stretching (LBSHS). WLBSHS uses local and global enhancement in weighted approach. Main purpose of local enhancement is sharpening edges of objective and exploring local information. For global enhancement, Bidirectional Smooth Histogram Stretching (BSHS) method is used. We divide the histogram in two parts and use forward and backward gamma transform on these parts, with bin interval control mechanism. We develop the hybrid method which is use to enhance the contrast of image with preserving its brightness by making the mixture of global and local enhancement. In this research we considered AMBE, E, PSNR EME, BR parameters for evaluating the enhanced image. This hybrid method is found to be better than AWIE, AGCID and VHA.

- by Vijay Kotkar
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- Computer Science, Artificial Intelligence, Computer Vision, Image Processing

Digital watermark technology hides copyright information in digital images, effectively protecting the copyright of digital images. At present, the color image digital watermarking algorithm still has defects such as the inability to balance robustness, invisibility and the weak anti-attack ability. Aiming at the above problems, this paper studies the digital watermarking method based on discrete wavelet transform
and discrete cosine transform. Then this paper proposes a color image blind digital watermarking algorithm based on QR code. First, convert the color image from RGB space to YCbCr space, extract the Y component and perform the second-level discrete wavelet transform. secondly, block the LL2 subband and perform the discrete cosine transform. finally, use the embedding method to convert the watermark information after the Arnold transform embedded in the block. The experimental results show that the PSNR of the color image embedded with the QR code is 56.7159 without being attacked. After being attacked, its PSNR and NC values are respectively 30dB and 0.95 or more, which proves that the algorithm has good robustness and can achieve watermarking blind extraction.

These days, videos can be easily recorded, altered and shared on social and electronic media for deception and false propaganda. However, due to sophisticated nature of the content alteration tools, alterations remain inconspicuous to the naked eye and it is a challenging task to differentiate between authentic and tampered videos. During the process of video tampering the traces of objects, which are removed or modified, remain in the frames of a video. Based on this observation, in this study, a new method is introduced for discriminating authentic and tampered video clips. This method is based on deep model, which consists of three types of layers: motion residual (MR), convolutional neural network (CNN), and parasitic layers. The MR layer highlights the tampering traces by aggregation of frames. The CNN layers encode these tampering traces and are learned using transfer learning. Finally, parasitic layers classify the video clip (VC) as authentic or tampered. The parasitic layers are learned using an efficient learning method based on extreme learning theory; they enhance the performance in terms of efficiency and accuracy. Intensive experiments were performed on various benchmark datasets to validate the performance and the robustness of the method; it achieved 98.89% accuracy. Comparative analysis shows that the proposed method outperforms the state-of-the-art methods. INDEX TERMS Spatial forgery detection, motion residual, deep learning, extreme learning machine, parasitic learning.

In this paper a quaternion approach of enhancement method is proposed in which color in the image is considered as a single entity. This new method is referred as the alpha-rooting method of color image enhancement by the two-dimensional quaternion discrete Fourier transform (2-D QDFT) followed by a spatial transformation. The results of the proposed color image enhancement method are compared with its counterpart channel-by-channel enhancement algorithm by the 2-D DFT. The image enhancements are quantified to the enhancement measure that is based on visual perception referred as the color enhancement measure estimation (CEME). The preliminary experiment results show that the quaternion approach of image enhancement is an effective color image enhancement technique.

We present a machine vision system for simultaneous and objective evaluation of two important functional attributes of a fabric, namely, soil release and shrinkage. Soil release corresponds to the efficacy of the fabric in releasing stains after laundering and shrinkage essentially quantifies the dimensional changes in the fabric postlaundering. Within the framework of the proposed machine vision scheme, the samples are prepared using a prescribed procedure and subsequently digitized using a commercially available off-the-shelf scanner. Shrinkage measurements in the lengthwise and widthwise directions are obtained by detecting and measuring the distance between two pairs of appropriately placed markers. In addition, these shrinkage markers help in producing estimates of the location of the center of the stain on the fabric image. Using this information, a customized adaptive statistical snake is initialized, which evolves based on region statistics to segment the stain. Once the stain is localized, appropriate measurements can be extracted from the stain and the background image that can help in objectively quantifying stain release. In addition, the statistical snakes algorithm has been parallelized on a graphical processing unit, which allows for rapid evolution of multiple snakes. This, in turn, translates to the fact that multiple stains can be detected and segmented in a computationally efficient fashion. Finally, the aforementioned scheme is validated on a sizeable set of fabric images and the promising nature of the results help in establishing the efficacy of the proposed approach.

- by Sridharan Kamalakannan
- •
- Soil Science, Parallel Algorithms, Parallel Computing, Computer Vision

Color image enhancement is a complex and challenging task in digital imaging with abundant applications. Preserving the hue of the input image is crucial in a wide range of situations. We propose simple image enhancement algorithms, which conserve the hue and preserve the range (gamut) of the R, G, B channels in an optimal way. In our setup, the intensity input image is transformed into a target intensity image whose histogram matches a specified, well-behaved histogram. We derive a new color assignment methodology where the resulting enhanced image fits the target intensity image. We analyze the obtained algorithms in terms of chromaticity improvement and compare them with the unique and quite popular histogram-based hue and range preserving algorithm of Naik and Murthy. Numerical tests confirm our theoretical results and show that our algorithms perform much better than the Naik-Murthy algorithm. In spite of their simplicity, they compete with well-established alternative methods for images where hue-preservation is desired.

- by Mila Nikolova
- •
- Color Image Enhancement

We introduce an efficient method to improve the quality of images which are captured underwater and images which are degraded by absorption and scattering of light. This method requires only one image that does not need any information about the underwater conditions or any specific type of hardware. The image is enhanced by reducing noise levels, better exposure of dark regions, enhanced global contrast and edges are enhanced significantly. It enhances the image by combining two images which are derived from the output of color enhancement and white-balancing result of the real image.

- by IJCSMC Journal and +1
- •
- Information Technology, Technology, Computer Engineering, Underwater Archaeology

In the literature a large number of linear and nonlinear denoising approaches for ultrasonic B-mode
images. The main purpose of this paper is to test the effect of hybridization of the Log Gabor filter with the
otheapproaches. The log-Gabor functions, by definition, always have no DC component, and secondly, the
transfer function of the log Gabor function has an extended tail at the high frequency end. Results show
that thhybridization of the Log Gabor with the Median filter gives the best output images and PSNR output
values.

- by Suja Priyadharsini .S
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- Computer Software, Color Image Enhancement, Color Image, Image Enhancement