Histogram equalization

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

A system and method for processing images of living tissue

Abstract: A system and method for processing images of living tissue diseases is described. The system includes a computer device for controlling its operation. An operator control device is coupled to the computer device. A viewing screen is coupled to the computer device for displaying digitized images of the living tissue. The operator, using the control device, selects desired portions of the digitized image for further image enhancement according to a desired image enhancement feature selectable from a plurality of image enhancement features. The image enhancement features include any combination of grey scale stretching, contrast enhancement based on logarithmic histogram equalization, spot enhancement and magnification. The system further includes means for visualization and quantification of micro-calcifications, and means for visualization and quantification of mass spiculations.

Tone reproduction in color imaging systems by histogram equalization of macro edges

Abstract: Tone reproduction attempts to scale or map high dynamic range image data such that the resulting image has preserved the visual brightness and better contrast impression of the original scenes. In this paper, we propose a systematic approach to tone reproduction by histogram equalization of macro edges. The proposed approach has been integrated with a robust image processing pipeline, and the experimental results show that the proposed algorithm is very stable for accommodating diverse illuminants and scenes.

Image Retrieval Using Maximum Frequency of Local Histogram Based Color Correlogram

Abstract: Color histogram is widely used for image indexing in content-based image retrieval (CBIR). A color histogram describes the global color distribution of an image. It is very easy to compute and is insensitive to small changes in viewing positions. However, the histogram is not robust to large appearance changes. Moreover, the histogram might give similar results for different kinds of images if the distributions of colors are same in the images. On the other hand, color correlogram is efficiently used for image indexing in content-based image retrieval. Color correlogram extracts not only the color distribution of pixels in images like color histogram, but also extracts the spatial information of pixels in the images. The characteristic of the color Correlogram to take into account the spatial information as well as the distribution of color pixels greatly attracts the researcher for content based image retrieval. In this paper, we propose the image bin (histogram value divisions) separation technique followed by extracting maxima of frequencies and plotting a correlogram. At first, the histogram is first calculated for an image. After that, it is subdivided into four equal bins. Each bin is subdivided into four more bins and for every such subdivision the maxima of frequencies s calculated. This information is stored in the form of a correlogram. The distance between correlogram of the query image with the corresponding correlogram of database images is calculated. The proposed algorithm is tested on a database comprising a large number of images.

Histogram equalization-based techniques for contrast enhancement of MRI brain Glioma tumor images: Comparative study

Abstract: In Magnetic Resonance Imaging (MRI), the poor images quality, particularly the artifacts inherent to this type of images as well as the low contrast between tissues and inter-individual variability, could make difficult the image analysis and affect the accuracy of clinical diagnosis. Therefore, the needs for image enhancement techniques arise to improve the relevant image contents through reducing the noise while preserving the actual details features. Various MRI images denoising techniques have been proposed in literature where each technique has its advantages and limitations. Among them, the Histogram modifications-based approaches arise as the most employed, by many researchers, for MRI contrast enhancement. This paper presents a comparative study of the most histogram-based techniques, mainly AHE, CLAHE, BPDHE and AIR-AHE techniques, dealing with denoising and contrast enhancement MRI images. Experimental study, using real-world databases, is performed based on evaluation of quality measurement metrics: absolute mean brightness error (AMBE), peak signal to noise ratio (PSNR) and Entropy. The studied histogram-based technique's advantages and limitations are also discussed.

New image enhancement algorithm for night vision

Abstract: In order to improve the clearness of images captured at night, a new enhancement algorithm is proposed in this paper. Its characters are: (1) enhancing the dark image with contrast enhancement and histogram equalization in series, so both the local and global information can be used; (2) a contrast transforming function which is suited to deal with dark image is proposed; (3) it can be realized in real time. The new method shows good performance in dealing with night vision images.