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Natural Enhancement of Color Image  [cached]
Chen Shaohua,Beghdadi Azeddine
EURASIP Journal on Image and Video Processing , 2010,
Abstract: A new algorithm of Natural Enhancement of Color Image (NECI) is proposed. It is inspired by multiscale Retinex model. There are four steps to realize this enhancement. At first, the image appearance is rendered by content-dependent global mapping for light cast correction, and then a modified Retinex filter is applied to enhance the local contrast. Histogram rescaling is used afterwards for normalization purpose. At last, the texture details of image are enhanced by emphasizing the high-frequency components of image using multichannel decomposition of Cortex Transform. In the contrast enhancement step, luminance channel is firstly enhanced, and then a weighing map is calculated by collecting luminance enhancement information and applied to chrominance channel in color space CIELCh which enables a proportional enhancement of chrominance. It avoids the problem of unbalanced enhancement in classical RGB independent channel operation. In this work, it is believed that image enhancement should avoid dramatic modifications to image such as light condition changes, color temperature alteration, or additional artifacts introduced or amplified. Disregarding light conditions of the scene usually leads to unnaturally sharpened images or dramatic white balance changes. In the proposed method, the ambience of image (warm or cold color impression) is maintained after enhancement, and no additional light sources are added to the scene, and no halo effect and blocking effect are amplified due to overenhancement. It realizes a Natural Enhancement of Color Image. Different types of natural scene images have been tested and an encouraging performance is obtained for the proposed method.
Multi-scale Color Image Enhancement Algorithm Based on Human Visual System (HVS)
基于人眼视觉特性的多尺度彩色图象增强算法

HUANG Kai-qi,WANG Qiao,WU Zhen-yang,
黄凯奇
,王桥,吴镇扬,胡学龙

中国图象图形学报 , 2003,
Abstract: Image enhancement techniques are used to process an image so that the final results are more suitable than the original image for human perceptual. Based on the analysis of color space and color components, we present a novel algorithm based on wavelet decomposition for color image enhancement. The perceptual color space HSV (Hue-Saturation-Value) is chosen according to human visual properties, in this color space, a hybrid contrast enhancement algorithm for color image, which used to adjust the luminance of image adaptively, in step of what follows, the saturation component of the image is stretching too. Color image enhancement exists two goals: image naturally and looking vividly as well as the distinguished details. Compared with other enhancement methods, experimental results confirm that our method improves color image quality from details as well as color components.
Comparative Study of Image Enhancement Techniques  [PDF]
Seema Rajput,Prof. S.R.Suralkar
International Journal of Computer Science and Mobile Computing , 2013,
Abstract: Fingerprints are the oldest and most widely used form of biometric identification. The performance of any fingerprint recognizer highly depends on the fingerprint image quality. Different types of noises in the fingerprint images pose greater difficulty for recognizers. However, fingerprint images are rarely of perfect quality. They may be degraded and corrupted due to variations in skin and impressionconditions. Thus, image enhancement techniques are employed prior to minutiae extraction to obtain a more reliable estimation of minutiae locations. Most Automatic Fingerprint Identification Systems (AFIS) use some form of image enhancement. Therefore, this paper describes various techniques for fingerprint image enhancement.
A Survey on Color Image Segmentation Techniques
彩色图像分割方法综述

LIN Kai yan,WU Jun hui,XU Li hong,LIN Kai yan,WU Jun hui,XU Li hong,LIN Kai yan,WU Jun hui,XU Li hong,
林开颜
,吴军辉,徐立鸿

中国图象图形学报 , 2005,
Abstract: Due to color image providing more information than monochrome image, color image processing is being paid more and more attention. Image segmentation is critical to image processing and pattern recognition, so all the typical approaches are presented and discussed in this paper. Basically, color image segmentation techniques are based on monochrome ones operating in different color spaces. This paper first reviewed some major color representation methods, then summarized the major color image segmentation approaches including histogram thresholding, characteristic feature clustering, region based approaches, edge detection, fuzzy techniques, neural networks, physics based method. The merits and drawbacks of the methods were discussed too. Fuzzy set theory provides a mechanism to present and manipulate uncertainty and ambiguity, which is desirable for image processing. So, the fuzzy approaches will have a promising application in the color image segmentation area.
A Comprehensive Review of Image Enhancement Techniques  [PDF]
Raman Maini,Himanshu Aggarwal
Computer Science , 2010,
Abstract: Principle objective of Image enhancement is to process an image so that result is more suitable than original image for specific application. Digital image enhancement techniques provide a multitude of choices for improving the visual quality of images. Appropriate choice of such techniques is greatly influenced by the imaging modality, task at hand and viewing conditions. This paper will provide an overview of underlying concepts, along with algorithms commonly used for image enhancement. The paper focuses on spatial domain techniques for image enhancement, with particular reference to point processing methods and histogram processing.
Review Of Various Image Contrast Enhancement Techniques  [PDF]
VIJAY A. KOTKAR, SANJAY S.GHARDE
International Journal of Innovative Research in Science, Engineering and Technology , 2013,
Abstract: Image Enhancement Is A Processing On An Image In Order To Make It More Appropriate For Certain Applications. It Is Used To Improve The Visual Effects And The Clarity Of Image Or To Make The Original Image More Conducive For Computer To Process. Contrast Enhancement Changing The Pixels Intensity Of The Input Image To Utilize Maximum Possible Bins. We Need To Study And Review The Different Image Contrast Enhancement Techniques Because Contrast Losses The Brightness In Enhancement Of Image. By Considering This Fact, The Mixture Of Global And Local Contrast Enhancement Techniques May Enhance The Contrast Of Image With Preserving Its Brightness. There Are Many Image Contrast Enhancement Techniques Such As HE, BBHE, DSIHE, MMBEBHE, RMSHE, MHE. BPDHE, RSWHE, GHE, LHE And LGCS. This Paper Focuses On The Comparative Study Of Contrast Enhancement Techniques With Special Reference To Local And Global Enhancement Techniques. Also Proposed Solution Is Identified To Apply To This Enhancement Technique. This Novel Method Will Use In Many Fields, Such As Medical Image Analysis, Remote Sensing, HDTV, Hyper Spectral Image Processing, Industrial X-Ray Image Processing, Microscopic Imaging Etc.
Review on Image Enhancement Techniques: FPGA Implementation perspective  [PDF]
Nirmala S. O,T. D. Dongale,R. K. Kamat
International Journal of Electronics Communication and Computer Technology , 2012,
Abstract: Extensive research has been done on image enhancement and hence it has become essential to categorize the research outcomes and provide an overview of the available enhancement techniques. In this paper different image enhancement techniques with their conceptual details are reviewed. The broad categorization of the reviewed algorithms is brought out with the emphasis on the state of art research in each category. The paper emphasizes on the review of Field Programmable Gate Array (FPGA) implementation of the image enhancement techniques.
Comparative Study of Different Image Enhancement Techniques  [cached]
Saruchi Garg,Madan Lal
International Journal of Computers & Technology , 2012,
Abstract: The main purpose of image enhancement is to bring out detail that is hidden in an image or to increase contrast in a low contrast image. Image enhancement techniques provide a multitude of choices for improving the visual quality of images. Appropriate choice of such techniques is greatly influenced by the imaging modality, task at hand and viewing conditions. This paper analyses the performance of some of existing image enhancement algorithms. The performance of algorithms are evaluated both qualitatively and quantitatively.
Overview of techniques used for image resolution enhancement  [PDF]
Mayuri D Patil,Prof. Surbhi Khare
International Journal on Computer Science and Engineering , 2012,
Abstract: Image resolution enhancement is one of the first steps in image processing. Image resolution enhancement is the process of manipulating an image so that resultant image is more suitable than the original one for specific application. Image enhancement can be done in various domains. For image resolution enhancement there are many methods, out of which image interpolation scheme is one of themost effective method. However, resolution is vital aspect of any image. Good quality image i.e. high resolution image produces better result in image processing applications. An interpolation is thetechnique to increase the resolution of the image by selecting new pixel from surrounding one. Image interpolation in complex wavelet transform is produces better results for any imaging application.DTCWT technique is used to improve the resolution of any satellite image. The paper focuses on the different techniques that are used to increase resolution of the images and their comparative results.
Image Enhancement Based on Color Histogram and DCT Approach  [PDF]
Pharindra kumar Sharma,Shalabh Agarwal,Piyush Shrivastava
International Journal of Computer Technology and Applications , 2011,
Abstract: This paper presents a new approach for color enhancement of the images that is based on the compressed domain techniqueand histogram equalization. My proposed technique is simple but more effective than some of old existing techniques like AR,MSE and SF-CES. We use the treatment of the chromatic components, while previous techniques treated only the luminance component. Also it is computationally more efficient than the spatial domain based method, so it is provide better color enhancement compressed domain based approaches.
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