Image enhancement using fuzzy intensity measure and adaptive clipping histogram equalization

Image enhancement aims at processing an input image so that the visual content of the output image is more pleasing or more useful for certain applications. Although histogram equalization is widely used in image enhancement due to its simplicity and effectiveness, it changes the mean brightness of the enhanced image and introduces a high level of noise and distortion. To address these problems, this paper proposes image enhancement using fuzzy intensity measure and adaptive clipping histogram equalization (FIMHE). FIMHE uses fuzzy intensity measure to first segment the histogram of the original image, and then clip the histogram adaptively in order to prevent excessive image enhancement. Experiments on the Berkeley database and CVF-UGR-Image database show that FIMHE outperforms state-of-the-art histogram equalization based methods

Fusion based Image Enhancement Approach for Brain Tumor Detection

Magnetic Resonance Imaging (MRI), is a crucial technology used in the processing of medical images that provides insights into the anatomy of soft organs in the human body and helps in detecting brain tumors and spinal tumors. Despite advances in technology, most images have intrinsic drawbacks such as reduced contrast and brightness, and noise. Several contrast enhancement techniques are used such as, HE, BBHE, DSIHE, CLAHE, RMSHE, and their fusion, have been deployed on different MRI images to handle these problems. Metrics such as, entropy, PIQE and BRISQUE are used in the assessment of the results. Through the different fusion combinations, most prominent results are obtained from CLAHE-RMSHE fusion with an entropy value of 6.2516 and BRISQUE value of 40.14

A Retinex-based Image Enhancement Scheme with Noise Aware Shadow-up Function

This paper proposes a novel image contrast enhancement method based on both a noise aware shadow-up function and Retinex (retina and cortex) decomposition. Under low light conditions, images taken by digital cameras have low contrast in dark or bright regions. This is due to a limited dynamic range that imaging sensors have. For this reason, various contrast enhancement methods have been proposed. Our proposed method can enhance the contrast of images without not only over-enhancement but also noise amplification. In the proposed method, an image is decomposed into illumination layer and reflectance layer based on the retinex theory, and lightness information of the illumination layer is adjusted. A shadow-up function is used for preventing over-enhancement. The proposed mapping function, designed by using a noise aware histogram, allows not only to enhance contrast of dark region, but also to avoid amplifying noise, even under strong noise environments.Comment: To appear in IWAIT-IFMIA 201

Performance Analysis of HE Methods for Low Contrast Images

AbstractThe image enhancement is one of the important issues in image processing. The main purpose is to highlight certain characteristic of image such as: contrast, sharpening. Histogram equalization is the well-known method for image enhancement. Histogram equalization became a popular technique because it is simple and effective. However Histogram equalization cause excessive contrast enhancement which cause visual artifacts of processed image. In this paper new forms of histogram equalization are overviewed to overcome this drawback. The major difference among the methods is the way to divide the input histogram. Recursive exposure based sub-image histogram equalization (R_ESIHE) use average intensity value as the separating point. Median-mean based sub-image clipped histogram equalization (MMSICHE) and Quadrants dynamic histogram equalization for contrast enhancement (QDHE) use median intensity value as separating point. Here objective parameters are Peak signal to noise ratio (PSNR) and Absolute Mean Brightness Error (AMBE)used to compare the quality of enhancement

An Adaptive Contrast Enhancement Algorithm with Details Preserving

This paper presents an adaptive contrastenhancement algorithm with details preserving (ACEDP) toenhance gray-scale image. Initially, the input image is classifiedinto low-, middle- or high-level image based on the gray-leveldistribution of maximum number of pixels. The proposedACEDP algorithm assigns different plateau functions fordifferent type of image and histogram clipping is then performedfollowed by histogram equalization. Simulation results show thatthe proposed technique outperforms several techniques inliterature. It demonstrates good ability in contrast enhancementas well as details preservation