Fuzzy inference based contextual dissimilarity histogram equalization algorithm for image enhancement

Abstract

In order to overcome the drawback of the existing image enhancement technologies and further consider the pixel intensity expression error caused by imaging, a novel fuzzy inference‐based contextual dissimilarity histogram equalization (FICDHE) algorithm is proposed. The proposed algorithm is composed of three modules. In the first module, according to the calculated probable intensity intervals, the membership functions of intensity are generated. In the second one, fuzzy inference systems are established and the contextual dissimilarity of each pixel is calculated. In the third module, the contextual dissimilarity histograms are clipped and equalized. The fuzzy system established in this paper not only fully considers the uncertainty source of pixel gray level expression, but also has adaptability. The parameter selection of fuzzy inference system membership function in this algorithm does not need human intervention, but is automatically obtained based on the statistical information of image pixel gray. Its adaptability makes the algorithm more widely used and convenient. Experiments are conducted using four typical medical images and 800 images from the KNIX dataset and BrainWeb dataset. The performance of the proposed method was compared to a series of enhancement algorithms based on both subjective judgment and image quality measurement indexes. Experimental results demonstrate that the proposed algorithm has a better contrast enhancement ability and yields better performance.

Feature extraction algorithm of an irregular small celestial body in a weak light environment

“…The primary purposes of image enhancement is to improve the quality and recognition of the image, highlight the parts or features of interest in the image, suppress secondary or unserviceable information in the image, and make the image easy to observe and further analyze and process ( Li et al, 2021 ). To address the problem of uneven light and low overall brightness of target celestial images captured by navigation cameras, we propose an improved histogram equalization image enhancement algorithm (HEEF) based on edge information fusion ( Dong, Ding & Xu, 2018 ).…”

Image Improvement via Fuzzy Inference Centered Contextual-Dissimilarity Histogram Equalization

“…LHE may introduce checkerboard artifacts during sub-image fusion. "Fuzzy Contextual Contrast Enhancement" (FCCE) creates a fuzzy dissimilarity histogram from fuzzy contrast factor [21]. The final improved image is based on the fuzzy dissimilarity index and a variant of the original image.…”