Histogram equalization

About: Histogram equalization is a research topic. Over the lifetime, 5755 publications have been published within this topic receiving 89313 citations.

Facial expression recognition using optimized active regions

Abstract: In this paper, we report an effective facial expression recognition system for classifying six or seven basic expressions accurately. Instead of using the whole face region, we define three kinds of active regions, i.e., left eye regions, right eye regions and mouth regions. We propose a method to search optimized active regions from the three kinds of active regions. A Convolutional Neural Network (CNN) is trained for each kind of optimized active regions to extract features and classify expressions. In order to get representable features, histogram equalization, rotation correction and spatial normalization are carried out on the expression images. A decision-level fusion method is applied, by which the final result of expression recognition is obtained via majority voting of the three CNNs’ results. Experiments on both independent databases and fused database are carried out to evaluate the performance of the proposed system. Our novel method achieves higher accuracy compared to previous literature, with the added benefit of low latency for inference.

Research of Image Retrieval Based on Color

Abstract: Research on image retrieval technology based on color feature, for the color histogram with a rotation, translation invariance of the advantages and disadvantages of lack of space, a color histogram and color moment combination Image Retrieval. The theory is a separate color images and color histogram moment of extraction, and then two methods of extracting color feature vector weighted to achieve similar distance, similar to the last distance based on the size of the return search results, based on the realization of the characteristics of the color image Retrieval system. The results show that the method is rotation, translation invariance, a single method of extracting color features, enhanced image search and improve the accuracy of the sort

Real-time underwater image enhancement: An improved approach for imaging with AUV-150

Abstract: An RGB YCbCr Processing method (RYPro) is proposed for underwater images commonly suffering from low contrast and poor color quality. The degradation in image quality may be attributed to absorption and backscattering of light by suspended underwater particles. Moreover, as the depth increases, different colors are absorbed by the surrounding medium depending on the wavelengths. In particular, blue/green color is dominant in the underwater ambience which is known as color cast. For further processing of the image, enhancement remains an essential preprocessing operation. Color equalization is a widely adopted approach for underwater image enhancement. Traditional methods normally involve blind color equalization for enhancing the image under test. In the present work, processing sequence of the proposed method includes noise removal using linear and non-linear filters followed by adaptive contrast correction in the RGB and YCbCr color planes. Performance of the proposed method is evaluated and compared with three golden methods, namely, Gray World (GW), White Patch (WP), Adobe Photoshop Equalization (APE) and a recently developed method entitled “Unsupervised Color Correction Method (UCM)”. In view of its simplicity and computational ease, the proposed method is recommended for real-time applications. Suitability of the proposed method is validated by real-time implementation during the testing of the Autonomous Underwater Vehicle (AUV-150) developed indigenously by CSIR-CMERI.

Comparative Analysis of Various Image Enhancement Techniques

Abstract: Image Enhancement is simple and most appealing area among all the digital image processing techniques. 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. Histogram equalization is one of the well known image enhancement technique. HE becomes a popular technique for contrast enhancement because this method is simple and effective. This paper represents review of some techniques in the area of image enhancement for brightness preservation as brightness preservation is in great demand in the consumer electronics field, when the image is effectively enhanced. Comparisons with the best available results are given in order to illustrate the best possible technique that can be used as powerful image enhancement. The performance of several established image enhancement techniques is presented in terms of different parameters like Absolute mean brightness error (AMBE), Peak signal to noise ratio (PSNR), Normalized absolute error (NAE), contrast, correlation and visual quality to make real-time image-processing applications more feasible and easier.

A new beta differential evolution algorithm for edge preserved colored satellite image enhancement

Abstract: Image enhancement plays a very crucial role in many image processing applications. It aims at improving the visual and informational quality of the distorted images. Histogram equalization is one of the most frequently used techniques for image contrast enhancement. However, histogram and most of the other enhancement approaches may yield un-natural looking or artifacts after enhancement, and the images computed by these methods are not desirable in few applications such as consumer electronic products where brightness preservation is necessary to avoid annoying artifacts. To overcome such problems, a new optimal grey level mapping based edge preserved satellite images enhancement technique using a beta differential evolution (BDE) algorithm has been proposed in this paper. The proposed method uses a simple grey-level mapping technique and beta differential evolution algorithm together with corresponding enhancement operators for quality contrast and brightness boosting of the satellite images. In this approach, the grey levels of the input image are replaced by a new set of grey levels. The proposed algorithm has been tested on numerous colored satellite images and also on standard Lena image. Further qualitative and statistical comparisons of the proposed BDE with artificial bee colony, modified artificial bee colony, particle swarm optimization, differential evolution algorithms are presented in the paper, which have proven its superiority in terms of PSNR, MSE, SSIM, FSIM and EKI indices.