A fast and efficient color image enhancement method based on fuzzy-logic and histogram

A new fuzzy logic and histogram based algorithm for enhancing low contrast color images has been proposed here. The method is computationally fast compared to conventional and other advanced enhancement techniques. It is based on two important parameters M and K, where M is the average intensity value of the image, calculated from the histogram and K is the contrast intensification parameter. The given RGB image is converted into HSV color space to preserve the chromatic information contained in the original image. To enhance the image, only the V component is stretched under the control of the parameters M and K. The proposed method has been compared with conventional contrast enhancement techniques as well as with advanced algorithms. All the above techniques were based on the principle of transforming the skewed histogram of the original image into a uniform histogram. The performance of the different contrast enhancement algorithms are evaluated based on the visual quality, Tenengrad, CII and the computational time. The inter comparison of different techniques was carried out on different low contrast color images. Based on the performance analysis, we advocate that our proposed Fuzzy Logic method is well suited for contrast enhancement of low contrast color images.     

Contextual information enhanced convolutional neural networks for retinal vessel segmentation in color fundus images

Accurate retinal vessel segmentation is a challenging problem in color fundus image analysis. An automatic retinal vessel segmentation system can effectively facilitate clinical diagnosis and ophthalmological research. In general, this problem suffers from various degrees of vessel thickness, perception of details, and contextual feature fusion in technique. For addressing these challenges, a deep learning based method has been proposed and several customized modules have been integrated into the well-known U-net with encoder–decoder architecture, which is widely employed in medical image segmentation. In the network structure, cascaded dilated convolutional modules have been integrated into the intermediate layers, for obtaining larger receptive field and generating denser encoded feature maps. Also, the advantages of the pyramid module with spatial continuity have been taken for multi-thickness perception, detail refinement, and contextual feature fusion. Additionally, the effectiveness of different normalization approaches has been discussed on different datasets with specific properties. Finally, sufficient comparative experiments have been enforced on three retinal vessel segmentation datasets, DRIVE, CHASE_DB1, and the STARE dataset with unhealthy samples. As a result, the proposed method outperforms the work of predecessors and achieves state-of-the-art performance.     

Classification of farmland images based on color features

Farmland images recognition and classification are of great significance in farmland environmental perception. Since the open and unstructured farmland environment has complex scenes, and is easily affected by various factors, furthermore, environmental information is uncertain and hard to predict. Based on hue saturation value (HSV), hue saturation lightness (HSL) and hue saturation intensity (HSI) color space models, taking use of image analysis and classification technology, this paper realizes the classification of farmland images in different environments. On the basis of color space, eight color features of the images are extracted. First, we conducted non equal interval quantification and drew the color feature curves, after that, we selected five eigenvectors which can correctly classify the images. Then, principal component analysis (PCA) was used for dimension reduction. Finally, radial basis function (RBF) neural network was joined for the extraction of images in the same scenes and different ones. The performance of the use of multiple color spaces combining with PCA and RBF shows that the average recognition rates of sunny days and cloudy days in the same scenes and different scenes are 100%, 87.36% and 84.58%, 68.11% respectively. Therefore, this method has higher recognition rate than BP neural network.     

基于颜色直方图的图像检索技术

讨论了基于颜色直方图的图像检索中颜色空间和直方图距离的选择问题,提出了利用颜色直方图来检索图像的一般框架,建立了基于两种颜色空间(RGB和HSV)和四种直方图距离共八种图像检索算法,并且采用了两种客观评价标准对所建立算法的有效性进行评价。结果证明,在基于颜色直方图的应用中,HSV空间比RGB空间更有效,EMD距离在所有四种距离量度中性能较好,但计算量稍大,与EMD距离相比,虽交集距离性能稍有不足,但所需计算量较小。     

Indexing and encoding based image feature representation with bin overlapped similarity measure for CBIR applications

In Content Based Image Retrieval (CBIR) system, the exhaustive search for a given query image to find the relevant images in the database are non-scalable. In this paper, we propose indexing, coding technique and similarity measure to address the above mentioned problem. We consider the color histogram of the image and its bin values are analyzed to understand the color information in the image. The histogram dimension is reduced by removing trivial bins and only those bins that represent color information significantly are considered. Based on the dimensions of the histogram, it is clustered and indexed. The Golomb–Rice (GR) coding is used to encode the indexed histograms. The Bin Overlapped Similarity Measure (BOSM) is proposed to compute the distance values between query and database image histograms. The performance of proposed approach is evaluated on benchmark datasets and found that the performance of the proposed approach is encouraging.     

基于德劳内三角剖分的彩色图像加权直方图表示及检索技术

经典的颜色直方图方法存在诸多缺陷，例如它不能表示图像中颜色的空间分布信息。为了进一步的提高图像检索能力，在分析图像特征的基础上，文章给出了一种基于角度图的直方图加权的图像表示方法。这种方法不仅保持了图像直方图简单方便的特点，同时又有效地将颜色的空间分布信息集成到直方图中。实验结果表明，对比经典直方图表示这种彩色图像的表示方法能获得更好的检索效果。     

融合亮度信息与色度信息的图像梯度提取方法

梯度信息对人眼视觉感知具有重要作用,准确的图像梯度描述对于图像边缘提取、图像增强、图像分割等都有着极为重要的意义。传统的梯度提取方法通常仅利用图像的亮度信息,当图像颜色发生改变而亮度变化不大时,传统方法获得的梯度数值则会与人眼的视觉感知产生矛盾。提出一种融合亮度信息与色度信息的图像梯度提取方法。该方法在保留亮度梯度信息的基础上,引入基于人眼视觉模型的CIE-L*a*b*颜色模型获得色度梯度信息,而后将归一化的亮度梯度与色度梯度进行了梯度融合。实验结果表明,该方法可充分利用亮度信息与色度信息,产生更符合人眼视觉感知的梯度描述。     

一种改进的火灾颜色模型检测算法

从火焰的颜色特征出发,综合考虑已有颜色模型的优缺点,提出一种改进的基于颜色模型的火灾检测方法.首先结合HSI颜色模型和YCbCr颜色模型对火灾图像进行预处理提取出可能火焰区域,然后在HSI空间上采用颜色空间距离法去除噪声.实验结果表明,该方法提高了火灾图像识别的准确度,并能在一定程度上消除干扰,具有较好的适用性.     

基于颜色和空间特征的彩色图像获取方法

本文针对基于内容的彩色图像相似性获取问题,提出了一种新的相似性获取方法,它既考虑了颜色内容特征,也包含了图像的空间关系特征.文章设计了无序矩阵来描述图像空间位置的相对关系,这种无序矩阵具有对旋转和平移不变性的特点.在进行图像间相似性测量时,首先用颜色直方图相交法对图像全局颜色分布进行相似性测量,再对得到的候选图像队列用新的算法进行测量,剔除不相似的图像.新的测量算法既考虑了颜色内容的匹配,同时也考虑了空间相似性的因素.用不同类型的图像对这种方法进行实验,其结果表明,它在彩色图像相似性检索时是很有效的,具有较高的检索精度.此外,它在图像发生小的旋转和平移等形变时,表现出较高的可靠性.     

Efficient edge-preserving algorithm for color contrast enhancement with application to color image segmentation

In this paper, a new and efficient edge-preserving algorithm is presented for color contrast enhancement in CIE Lu^′v^′ color space. The proposed algorithm not only can enhance the color contrast as the previous algorithm does, but also has an edge-preservation effect. In addition, the spurious edge points occurred due to the color contrast enhancement can be well reduced using the proposed algorithm. This is the first edge-preserving algorithm for color contrast enhancement in color space. Furthermore, a novel color image segmentation algorithm is presented to justify the edge-preservation benefit of the proposed color contrast enhancement algorithm. Based on some real images, experimental results demonstrate the advantages of color contrast enhancement, edge-preservation effect, and segmentation result in our proposed algorithm.     

Novel adaptive color space transform and application to image compression

Various contemporary standards by Joint Picture Expert Group, which is used for compression, exploited the correlation among the color components using a component color space transform before the subband transform stage. The transforms used to de-correlate the colors are primarily the fixed kernel transforms, which are not suitable for large class of images. In this paper an image dependent color space transform (ID-CCT), exploiting the inter-channel redundancy optimally and which is very much suitable for compression has been proposed. Also the comparative performance has been evaluated and a significant improvement has been observed, objectively as well as subjectively over other quantifiable methods.