Color Histogram Equalization Research Articles

Guava Disease Detection Using Deep Convolutional Neural Networks: A Case Study of Guava Plants

Food production is a growing challenge with the increasing global population. To increase the yield of food production, we need to adopt new biotechnology-based fertilization techniques. Furthermore, we need to improve early prevention steps against plant disease. Guava is an essential fruit in Asian countries such as Pakistan, which is fourth in its production. Several pathological and fungal diseases attack guava plants. Furthermore, postharvest infections might result in significant output losses. A professional opinion is essential for disease analysis due to minor variances in various guava disease symptoms. Farmers’ poor usage of pesticides may result in financial losses due to incorrect diagnosis. Computer-vision-based monitoring is required with developing field guava plants. This research uses a deep convolutional neural network (DCNN)-based data enhancement using color-histogram equalization and the unsharp masking technique to identify different guava plant species. Nine angles from 360∘ were applied to increase the number of transformed plant images. These augmented data were then fed as input into state-of-the-art classification networks. The proposed method was first normalized and preprocessed. A locally collected guava disease dataset from Pakistan was used for the experimental evaluation. The proposed study uses five neural network structures, AlexNet, SqueezeNet, GoogLeNet, ResNet-50, and ResNet-101, to identify different guava plant species. The experimental results proved that ResNet-101 obtained the highest classification results, with 97.74% accuracy.

Fuzzy color histogram equalization with weighted distribution for image enhancement

Low light often degrades the quality of an image, which in turn affects the computer vision algorithm’s performance. The development of image enhancement algorithms improves the visual quality and valuable details of an image. Nevertheless, the existing algorithms inevitably introduce unwanted artifacts and color distortion while preserving the tones and information. Implementing a new fuzzy color histogram equalization with a weighted distribution algorithm will address these drawbacks effectively and yields a better-quality image. In the proposed method, fuzzy dissimilarity histogram is constructed from the neighbourhood characteristics of an intensity to improve the contrast and naturalness of an image. Then, incorporate gamma correction for further enhancement in dark regions. Finally, modify the saturation to the permittable maximum saturation range to avoid the fading effect. The extended experimental results on different scenes demonstrate that the proposed algorithm can enhance the quality and details of an image efficiently. Objective measures show the competitive performance of the proposed algorithm compared with the other existing methods.

Fuzzy dissimilarity color histogram equalization for contrast enhancement and color correction

Many statistical histogram-based methods perform intensity transformation on gray levels in the statistical histogram. This may cause over-enhancement due to dominating portions of the histogram. Various methods tackle this problem by overwhelming the dominating portions and improving the inferior components. Though, this may change the natural appearances of the image and results in degraded visual quality. In order to attenuate such limitations, an efficient method called Fuzzy Dissimilarity Adaptive Histogram Equalization with Gamma Correction (FDAHE-GC) algorithm is proposed. In this work, a Fuzzy Dissimilarity Histogram (FDH) is obtained from the neighborhood characteristics of an intensity. An intensity mapping function, constructed from FDH is applied to enhance the contrast and natural characteristics of an image. Finally, the gamma correction is employed to enhance the dark regions. In order to tune the fine details and to improve visual appearance of an image, the proposed FDAHE-GC algorithm is applied to the intensity value of HSI space. The performance of the presented method is evaluated with different existing methods using image quality assessment tools such as entropy, Colorfulness (C), Hue Deviation Index (HDI), Saturation, Contrast Enhancement Factor (CEF) and Gradient (G). The investigational results tested on standard benchmark test images with visual inspection shows the superiority of the proposed FDAHE-GC algorithm.

Risk of falling in bilateral vestibulopathy: how should we predict this?

Low light often degrades the quality of an image, which in turn affects the computer vision algorithm’s performance. The development of image enhancement algorithms improves the visual quality and valuable details of an image. Nevertheless, the existing algorithms inevitably introduce unwanted artifacts and color distortion while preserving the tones and information. Implementing a new fuzzy color histogram equalization with a weighted distribution algorithm will address these drawbacks effectively and yields a better-quality image. In the proposed method, fuzzy dissimilarity histogram is constructed from the neighbourhood characteristics of an intensity to improve the contrast and naturalness of an image. Then, incorporate gamma correction for further enhancement in dark regions. Finally, modify the saturation to the permittable maximum saturation range to avoid the fading effect. The extended experimental results on different scenes demonstrate that the proposed algorithm can enhance the quality and details of an image efficiently. Objective measures show the competitive performance of the proposed algorithm compared with the other existing methods.

Hue-preserving and saturation-improved color histogram equalization algorithm.

In this paper, an algorithm is proposed to improve contrast and saturation without color degradation. The local histogram equalization (HE) method offers better performance than the global HE method, whereas the local HE method sometimes produces undesirable results due to the block-based processing. The proposed contrast-enhancement (CE) algorithm reflects the characteristics of the global HE method in the local HE method to avoid the artifacts, while global and local contrasts are enhanced. There are two ways to apply the proposed CE algorithm to color images. One is luminance processing methods, and the other one is each channel processing methods. However, these ways incur excessive or reduced saturation and color degradation problems. The proposed algorithm solves these problems by using channel adaptive equalization and similarity of ratios between the channels. Experimental results show that the proposed algorithm enhances contrast and saturation while preserving the hue and producing better performance than existing methods in terms of objective evaluation metrics.

3-D color histogram equalization by principal component analysis

Color histogram equalization is a method for improving visual appearance of images by enhancing image contrast. Color histogram equalization methods are mostly faced with problems like over-enhancement and brightening. In this paper a new color histogram equalization method is proposed which defines a new three dimensional cumulative distribution function based on a one-dimensional histogram. This one-dimensional histogram is calculated by taking into account the correlation between color channels using PCA. Over-enhancement and brightening are solved by this method because of applying the equalization on a transformed image instead of image itself.

Comparative Study on CBIR based on Color Feature

Content Based Image Retrieval (CBIR) system helps users to retrieve relevant images based on their contents. It finds images in large databases by using a unique image feature such as texture, color, intensity or shape of the object inside an image. This paper presents a comparative study between the feature extraction techniques that based on color feature. These techniques include Color Histogram, HSV Color Histogram and Color Histogram Equalization. In this study, the retrieval process is first done by measuring the similarities between the query image and the images within the WANG database using two approaches: Euclidean distance and correlation coefficients. Then, the comparison is carried out by measuring the accuracy, error rate and elapsed time of each technique.

An Improved Color Image Enhancement Algorithm Based on 3-D Color Histogram Equalization

This paper proposes an improved color image enhancement algorithm based on 3-D color histogram equalization algorithm. When the existed 3-D color histogram equalization algorithms in the literatures are applied in processing dim color images, the processed color images often turn pale due to the decrease of color-saturations and have false contours due to gray-scale merging phenomenon in the histogram equalization algorithm. In this paper, the proposed algorithm can make more pixels of the processed color images keep their color-saturations and reduce the gray-scale merging with Logarithmic histogram equalization algorithm. Test results with dim color images present a better effect of image enhancement.

Joint Depth Map and Color Consistency Estimation for Stereo Images with Different Illuminations and Cameras

Abstract—In this paper, we propose a method that infers both accurate depth maps and color-consistent stereo images for radiometrically varying stereo images. In general, stereo matching and performing color consistency between stereo images are a chicken-and-egg problem since it is not a trivial task to simultaneously achieve both goals. Hence, we have developed an iterative framework in which these two processes can boost each other. First, we transform the input color images to log-chromaticity color space, from which a linear relationship can be established during constructing a joint pdf of transformed left and right color images. From this joint pdf, we can estimate a linear function that relates the corresponding pixels in stereo images. Based on this linear property, we present a new stereo matching cost by combining Mutual Information (MI), SIFT descriptor, and segment-based plane-fitting to robustly find correspondence for stereo image pairs which undergo radiometric variations. Meanwhile, we devise a Stereo Color Histogram Equalization (SCHE) method to produce color-consistent stereo image pairs, which conversely boost the disparity map estimation. Experimental results show that our method produces both accurate depth maps and color-consistent stereo images, even for stereo images with severe radiometric differences.

基于暗原色先验的嵌入式图像增强系统

According to the disadvantage of color distortion of color histogram equalization algorithm,a new image enhancement algorithm based on dark channel prior is presented.First,the dark channel image is done.Then the transmission map and the atmospheric optical components map are estimated.Finally the bright values of the pixels of the image are calculated according to the atmospheric model.An embedded high speed image enhancement processing system on high performance DSP TMS320DM6437 and FPGA was designed.Experimental results with real images shown that the system can improve the contrast and color of the image,restore the real color of the image,and effectively enhance the image,the running time of the program is shorter,so it can meet the requirements in the project.

A Novel 3-D Color Histogram Equalization Method With Uniform 1-D Gray Scale Histogram

The majority of color histogram equalization methods do not yield uniform histogram in gray scale. After converting a color histogram equalized image into gray scale, the contrast of the converted image is worse than that of an 1-D gray scale histogram equalized image. We propose a novel 3-D color histogram equalization method that produces uniform distribution in gray scale histogram by defining a new cumulative probability density function in 3-D color space. Test results with natural and synthetic images are presented to compare and analyze various color histogram equalization algorithms based upon 3-D color histograms. We also present theoretical analysis for nonideal performance of existing methods.

색채 항상성 방법과 경계 영역 기반 히스토그램 평활화 방법을 이용한 영상의 화질 향상 방법

고선명 영상에 대한 수요가 증가하면서 다양한 방면에서 좀 더 선명하고 큰 영상을 보고 촬영하려는 요구가 늘어나고 있다. 특히 디스플레이 장치의 크기가 커지고 이에 따라 영상의 해상도가 커지면서 영상에서 나타나는 잡음이나 화질 저하가 이전에 비하여 더욱 더 눈에 띄게 나타나게 되었다. 본 논문에서 고선명 영상과 같이 해상도가 큰 영상의 색상과 명암 대비를 효과적이고 빠르게 개선하기 위한 방법을 제안한다. 고해상도 영상에서 처리 속도를 높이면서 효과적으로 화질 향상 방법을 적용하기 위해 고해상도 영상을 축소시킨 영상에서 화질 향상 방법에 필요한 변수를 추출해낸다. 영상의 색상을 향상시키기 위해 기존의 색채 항상성 방법을 개선시킨 방법을 적용하였고 명암 대비를 향상시키기 위해 경계 영역을 활용한 변형 히스토그램 평활화 방법을 적용하였다. 마지막으로 고해상도 영상을 촬영할 수 있는 디지털 캠코더를 이용하여 촬영한 실험 영상으로 제안하는 방법의 성능을 분석하였다. A unified image enhancement method is proposed for high-resolution image which based on color constancy and histogram equalization using edge region. To speed up the method, smaller image is used when parameters of color constancy and histogram equalization are determined. In the color constancy process, nth-derivative of gaussian is applied to x and y axis separately in order to estimate the color of the illumination rapidly. In the histogram equalization process, the histogram obtained from near-edge region is used for the histogram equalization. In the experiments, high-resolution images taken by digital camcorder are used for verifying the performance of the proposed method.

An Unsupervised Approach for Video Text Localization

A new video text localization approach is proposed. First, some pre-processing techniques, including color space conversion and histogram equalization, are applied to the input video frames to obtain the enhanced gray-scale images. Features are then extracted using wavelet transform to represent the texture property of text regions. Next, an unsupervised fuzzy c-means classifier is performed to discriminate candidate text pixels from background. Effective operations such as the morphological dilation operation and logical AND operation are applied for locating text blocks. A projection analysis technique is then employed to extract text lines. Finally, some geometric heuristics are used to remove noise regions and refine location of text lines. Experimental results indicate that the proposed approach is superior to other three representative approaches in term of total detection rate.

Adaptive-neighborhood histogram equalization of color images

Histogram equalization (HE) is one of the simplest and most effective techniques for enhancing gray-level images. For color images, HE becomes a more difficult task, due to the vectorial nature of the data. We propose a new method for color image enhancement that uses two hierarchical levels of HE: global and local. In order to preserve the hue, equalization is only applied to intensities. For each pixel (called the ‘‘seed’’ when being processed) a variable-sized, variable-shaped neighborhood is determined to contain pixels that are ‘‘similar’’ to the seed. Then, the histogram of the region is stretched to a range that is computed with respect to the statistical parameters of the region (mean and variance) and to the global HE function (of intensities), and only the seed pixel is given a new intensity value. We applied the proposed color HE method to various images and observed the results to be subjectively ‘‘pleasant to the human eye,’’ with emphasized details, preserved colors, and with the histogram of intensities close to the ideal uniform one. The results compared favorably with those of three other methods (histogram explosion, histogram decimation, and three-dimensional histogram equalization) in terms of subjective visual quality.

THE USE OF COLOR IN THE SCIENTIFIC VISUALIZATION OF ACOUSTICAL PHENOMENA

Color is commonly used in representing scientific data, especially as color output devices have become widely available. The misuse of color can lead to confusing or even misleading representations of data. The purpose of this paper is to provide a review of guidelines for using color effectively, specifically aimed at the computational acoustics community. The question of whether or not to use color at all is initially raised, and gray-scale is suggested for appropriate cases. Human color deficiencies in technical audiences are then described, and the consequential choices of equally-accessible color schemes are discussed. The topics of human color perception models, hypsometric tints, contoured plots, and histogram equalization are introduced. Color palettes specifically suited for acoustical phenomena, such as linear and dB magnitudes, scales for positive and negative data, circular palettes for phase, and methods for illustrating different classes of phenomena (e.g., wave types) on a single plot, are discussed in detail. Color examples and Mathematica code are provided, and final recommendations are made for choosing effective color schemes.

Secondary Ion Imaging with a CCD Camera

AbstractA digital image processing system based on a charge-coupled device (CCD) camera interfaced to a Texas Instruments Professional Computer (TIPC) has been implemented on the Cameca IMS-3F Ion Microscope to improve lateral imaging capabilities. The CCD sensor, produced at TI, generates a 376 × 288 pixel image, and a hardware interface to the TIPC provides an 8-bit grayscale (256 levels) for subsequent storage and display. The Cameca ion microscope projects secondary ions from the sample onto a microchannel plate/fluorescent screen combination. The CCD array images the screen directly and no vacuum modifications to the Cameca are required. Nitrogen cooling of the CCD imager to reduce thermal noise allows low light levels to be integrated for several minutes and displayed on the TIPC, providing a substantial increase in sensitivity over the standard optics. False color imaging enhances the contrast of various features in the ion image to provide ease of identification. A variety of software options, such as frame mosaicking, multiple frame overlays, color threshold modification and histogram equalization are available. The TIPC has also been interfaced to the Cameca controlling computer to allow display of ion images during depth profiling.