Contrast enhacenment of RGB color images by histogram equalization of color vectors' intensities

Mejora del contraste de imagenes de color RGBThe histogram equalization (HE) is a technique developed for image contrast enhancement of grayscale images. For RGB (Red, Green, Blue) color images, the HE is usually applied in the color channels separately; due to correlation between the color channels, the chromaticity of colors is modified. In order to overcome this problem, the colors of the image are mapped to different color spaces where the chromaticity and the intensity of colors are decoupled; then, the HE is applied in the intensity channel. Mapping colors between different color spaces may involve a huge computational load, because the mathematical operations are not linear. In this paper we present a proposal for contrast enhancement of RGB color images, without mapping the colors to different color spaces, where the HE is applied to the intensities of the color vectors. We show that the images obtained with our proposal are very similar to the images processed in the HSV (Hue, Saturation, Value) and L*a*b* color spaces

Local Contrast Enhancement Utilizing Bidirectional Switching Equalization Of Separated And Clipped Sub-Histograms

Digital image contrast enhancement methods that are based on histogram equalization (HE) technique are useful for the use in consumer electronic products due to their simple implementation. However, almost all the suggested enhancement methods are using global processing technique, which does not emphasize local contents. Kaedah penyerlahan beza jelas imej digit berdasarkan teknik penyeragaman histogram adalah berguna dalam penggunaan produk elektronik pengguna disebabkan pelaksanaan yang mudah. Walau bagaimanapun, kebanyakan kaedah penyerlahan yang dicadangkan adalah menggunakan teknik proses sejagat dan tidak menekan kepada kandungan setempat

Histogram specification of 24-bit color images in the color difference (C-Y) color space

Histogram equalization and specification have been widely used to enhance information in a gray scale image, with histogram specification having the advantage of allowing the output histogram to be specified as compared to histogram equalization, which attempts to produce an output histogram that is uniform Unfortunately, expanding histogram techniques to color images is not very straightforward Since humans are sensitive to chromatic changes, care must be taken to ensure that incorrect colors are not produced. Additionally expanding the one-dimensional histogram used in gray level histogram techniques to a joint histogram (usually of three variables representing the primary colors of red, green, and blue) can yield specified histograms which have no physical meaning, hence making if difficult to determine the set of histograms required for a desired enhancement in this paper, we describe a method of extending gray level histogram specification to color images by performing histogram specification an the luminance, saturation, and hue components in the color difference C-Y color space. These methods take into account the relationship between the luminance and saturation components while yielding specified histograms that produce natural-looking results

Importance of Image Enhancement Techniques in Color Image Segmentation: A Comprehensive and Comparative Study

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.Comment: 27 pages, 17 figures, 2 Tables, 1 flowchar

Segmentation of images by color features: a survey

En este articulo se hace la revisión del estado del arte sobre la segmentación de imagenes de colorImage segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown

Color Image Enhancement Techniques for Endoscopic images

Modern endoscopes play an important role in diagnosing various gastrointestinal (GI) tract related diseases. Although clinical findings of modern endoscopic imaging techniques are encouraging, there still remains much room for improvement of image quality. Of greatest concern, endoscopic images suffer from various degradations, such as specular highlights, non-uniform brightness and poor contrast. As a result, gastroenterologists often face difficulty in successfully identifying the subtle features, such as mucosal surface and structures, pit patterns, size and pattern of micro-vessels, tissue and vascular characteristics, superficial layer of mucosal and abnormal growths in endoscopic images. The improved visual quality of images can provide better diagnosis. This paper presents two proposed post-processing techniques for enhancing the subtle features of endoscopic images. The first proposed technique is named as endoscopic image enhancement based on adaptive sigmoid function and space-variant color reproduction (ASSVCR). It is achieved in two stages: image enhancement at gray level followed by color reproduction with the help of space variant chrominance mapping. Image enhancement is achieved by performing adaptive sigmoid function and uniform distribution of sigmoid pixels. Then color reproduction is used to generate new chrominance components. The second proposed technique is named as tri-scan. It is achieved in three stages: (1) Tissue and surface enhancement: a modified linear unsharp masking is used to sharpen the surface and edges of tissue and vascular characteristics, (2) Mucosa layer enhancement: an adaptive sigmoid function similar to the ASSVCR technique is employed on the R plane of the image to highlight the superficial layers of mucosa, (3) Color tone enhancement: the pixels are uniformly distributed to create a different color effect to highlight mucosa structures, superficial layers of mucosa and tissue characteristics. Both techniques are compared with other related works. Several performance metrics like focus value, statistic of visual representation, measurement of uniform distribution, color similarity test, color enhancement factor (CEF) and time complexity are used to assess the performance. The results showed improved performance compared to similar existing methods. In the post-processed images, we have observed that the ASSVCR can enhance and highlight pit patterns, tissue and vascular characteristics, mucosa structures and abnormal growths. It cannot highlight size and pattern of micro-vessels, and superficial layer of mucosa. In contrast, tri-scan can enhance and highlight all above mentioned features of endoscopic images

Camera Independent Face Recognition Algorithm In Visual Surveillance

Face recognition in visual surveillance has the ability to reduce crime rates in public area due to the suspect’s identity can be automatically identified in real-time using the face images captured by the surveillance camera as circumstantial evidence. Several available image preprocessing techniques, classifiers, and approaches had been proposed and tested to mitigate the effect of illumination variation, pose variations, and intensity quality differences due to hardware differences in such system. The face recognition system should be able to integrate seamlessly into the existing system. From the experiments, Histogram Equalization (HE) preprocessed face images scaled to 30�30 had proven to be well suited for pre-processing of surveillance images. The combination of Linear Discriminant Analysis (LDA) and HE preprocessed images managed to achieve an average recognition rate of 81.48% for the single camera training set. The flandmark facial landmark detector is implemented to determine the location of the eyes and new face images are obtained by cropping the HE pre-processed images. The combination of flandmark images at 20�30 with multi-class Support Vector Machine (SVM) is used to form a multimodal classification system with LDA and HE combination. Score level fusion is done to the normalized output scores of both the classifiers with proper weight, w assigned to each score. Finally, the watch list principle will list out several possible subjects according to their respective score ranking rather than deciding on a particular subject based on the maximum score, thus increasing the performance of the proposed system. The experimental results demonstrate the performance of the proposed algorithm on Surveillance Camera Face Database (SCface) database with 97.45% average recognition rate

Automatic Detection and Segmentation of Lentil Breeding Plots from Images Captured by Multi-spectral UAV-Mounted Camera

Automatic Detection and Segmentation of Lentil Breeding Plots from Images Captured by Multi-spectral UAV-Mounted Camer

Automatic Detection and Segmentation of Lentil Breeding Plots from Images Captured by Multi-spectral UAV-Mounted Camera

Automatic Detection and Segmentation of Lentil Breeding Plots from Images Captured by Multi-spectral UAV-Mounted Camer

Contrast enhancement and exposure correction using a structure-aware distribution fitting

Realce de contraste e correção de exposição são úteis em aplicações domésticas e técnicas, no segundo caso como uma etapa de pré-processamento para outras técnicas ou para ajudar a observação humana. Frequentemente, uma transformação localmente adaptativa é mais adequada para a tarefa do que uma transformação global. Por exemplo, objetos e regiões podem ter níveis de iluminação muito diferentes, fenômenos físicos podem comprometer o contraste em algumas regiões mas não em outras, ou pode ser desejável ter alta visibilidade de detalhes em todas as partes da imagem. Para esses casos, métodos de realce de imagem locais são preferíveis. Embora existam muitos métodos de realce de contraste e correção de exposição disponíveis na literatura, não há uma solução definitiva que forneça um resultado satisfatório em todas as situações, e novos métodos surgem a cada ano. Em especial, os métodos tradicionais baseados em equalização adaptativa de histograma sofrem dos efeitos checkerboard e staircase e de excesso de realce. Esta dissertação propõe um método para realce de contraste e correção de exposição em imagens chamado Structure-Aware Distribution Stretching (SADS). O método ajusta regionalmente à imagem um modelo paramétrico de distribuição de probabilidade, respeitando a estrutura da imagem e as bordas entre as regiões. Isso é feito usando versões regionais das expressões clássicas de estimativa dos parâmetros da distribuição, que são obtidas substituindo a mé- dia amostral presente nas expressões originais por um filtro de suavização que preserva as bordas. Após ajustar a distribuição, a função de distribuição acumulada (CDF) do modelo ajustado e a inversa da CDF da distribuição desejada são aplicadas. Uma heurística ciente de estrutura que detecta regiões suaves é proposta e usada para atenuar as transformações em regiões planas. SADS foi comparado a outros métodos da literatura usando métricas objetivas de avaliação de qualidade de imagem (IQA) sem referência e com referência completa nas tarefas de realce de contraste e correção de exposição simultâneos e na tarefa de defogging/dehazing. Os experimentos indicam um desempenho geral superior do SADS em relação aos métodos comparados para os conjuntos de imagens usados, de acordo com as métricas IQA adotadas.Contrast enhancement and exposure correction are useful in domestic and technical applications, the latter as a preprocessing step for other techniques or for aiding human observation. Often, a locally adaptive transformation is more suitable for the task than a global transformation. For example, objects and regions may have very different levels of illumination, physical phenomena may compromise the contrast at some regions but not at others, or it may be desired to have high visibility of details in all parts of the image. For such cases, local image enhancement methods are preferable. Although there are many contrast enhancement and exposure correction methods available in the literature, there is no definitive solution that provides a satisfactory result in all situations, and new methods emerge each year. In special, traditional adaptive histogram equalization-based methods suffer from checkerboard and staircase effects and from over enhancement. This dissertation proposes a method for contrast enhancement and exposure correction in images named Structure-Aware Distribution Stretching (SADS). The method fits a parametric model of probability distribution to the image regionally while respecting the image structure and edges between regions. This is done using regional versions of the classical expressions for estimating the parameters of the distribution, which are obtained by replacing the sample mean present in the original expressions by an edge-preserving smoothing filter. After fitting the distribution, the cumulative distribution function (CDF) of the adjusted model and the inverse of the CDF of the desired distribution are applied. A structure-aware heuristic to indicate smooth regions is proposed and used to attenuate the transformations in flat regions. SADS was compared with other methods from the literature using objective no-reference and full-reference image quality assessment (IQA) metrics in the tasks of simultaneous contrast enhancement and exposure correction and in the task of defogging/dehazing. The experiments indicate a superior overall performance of SADS with respect to the compared methods for the image sets used, according to the IQA metrics adopted