Histogram equalization

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

An artificial bee colony algorithm for image contrast enhancement

Abstract: Image Enhancement is a crucial phase in almost every image processing system It aims at improving both the visual and the informational quality of distorted images Histogram Equalization (HE) techniques are the most popular approaches for image enhancement for they succeed in enhancing the image and preserving its main characteristics However, using exhaustive approaches for histogram equalisation is an algorithmically complex task These HE techniques also fail in offering good enhancement if not so good parameters are chosen So, new intelligent approaches, using Artificial Intelligence techniques, have been proposed for image enhancement In this context, this paper proposes a new Artificial Bee Colony (ABC) algorithm for image contrast enhancement A grey-level mapping technique and a new image quality measure are used The algorithm has been tested on some test images, and the comparisons of the obtained results with the genetic algorithm have proven its superiority Moreover, the proposed algorithm has been extended to colour image enhancement and given very promising results Further qualitative and statistical comparisons of the proposed ABC to the Cuckoo Search (CS) algorithm are also presented in the paper; not only for the adopted grey-level mapping technique, but also with using another common transformation, generally called the local/global transformation

Underwater image enhancement with global–local networks and compressed-histogram equalization

Abstract: Due to the light absorption and scattering, captured underwater images usually contain severe color distortion and contrast reduction. To address the above problems, we combine the merits of deep learning and conventional image enhancement technology to improve the underwater image quality. We first propose a two-branch network to compensate the global distorted color and local reduced contrast, respectively. Adopting this global–local network can greatly ease the learning problem, so that it can be handled by using a lightweight network architecture. To cope with the complex and changeable underwater environment, we then design a compressed-histogram equalization to complement the data-driven deep learning, in which the parameters are fixed after training. The proposed compression strategy is able to generate vivid results without introducing over-enhancement and extra computing burden. Experiments demonstrate that our method significantly outperforms several state-of-the-arts in both qualitative and quantitative qualities.

A new approach to image retrieval with hierarchical color clustering

Abstract: After performing a thorough comparison of different quantization schemes in the RGB, HSV, YUV, and CIEL*u*v* color spaces, we propose to use color features obtained by hierarchical color clustering based on a pruned octree data structure to achieve efficient and robust image retrieval. With the proposed method, multiple color features, including the dominant color, the number of distinctive colors, and the color histogram, can be naturally integrated into one framework. A selective filtering strategy is also described to speed up the retrieval process. Retrieval examples are given to illustrate the performance of the proposed approach.

A novel approach for contrast enhancement based on Histogram Equalization

Abstract: Histogram based techniques is one of the important digital image processing techniques which can be used for image enhancement. One of the advantages of histogram based techniques is simplicity of implementation of the algorithm. Also it should be mentioned that histogram based techniques is much less expensive comparing to the other methods. Histogram based techniques for image enhancement is mostly based on equalizing the histogram of the image and increasing the dynamic range corresponding to the image. Histogram equalization (HE) method has two main disadvantages which affect efficiency of this method. For solving the above problems, some techniques have proposed for example using bi histogram equalization (BHE) algorithm instead of histogram equalization (HE). It should be mentioned that bi histogram equalization (BHE) is one of the best proposed algorithm which has proposed until now. This paper presents a novel algorithm for contrast enhancement based on histogram equalization (HE). Our proposed algorithm applies some preprocessing steps on the histogram corresponding to the image and then applies histogram equalization. We have applied our proposed algorithm on a database which includes 220 normal images and results are promising. Our proposed method has better results comparing with bi histogram equalization (BHE) algorithm based on visual criterion and a mathematical criterion.

A Comparison of the Multiscale Retinex With Other Image Enhancement Techniques

Abstract: The multiscale retinex with color restoration (MSRCR) has shown itself to be a very versatile automatic image enhancement algorithm that simultaneously provides dynamic range compression, color constancy, and color rendition. A number of algorithms exist that provide one or more of these features, but not all. In this paper we compare the performance of the MSRCR with techniques that are widely used for image enhancement. Specifically, we compare the MSRCR with color adjustment methods such as gamma correction and gain/offset application, histogram modification techniques such as histogram equalization and manual histogram adjustment, and other more powerful techniques such as homomorphic filtering and ''burning and dodging''. The comparison is carried out by testing the suite of image enhancement methods on a set of diverse images. We find that though some of these techniques work well for some of these images, only the MSRCR performs universally well on the test set.