Image Enhancement Based on Pulse Coupled Neural Network in the Nonsubsample Shearlet Transform Domain

Zhi, Qu; Xing, Yaqiong; Song, Yafei

doi:10.1155/2019/2641516

Cited by 5 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A technique for improved images combines histogram equalization with fuzzy set theory, which eliminates the frequent over-improvement and weakening of local information in standard enhancement algorithms [25]. An approach for improving the image, integrating PCNN, and fuzzy theory, has also been developed to boost contrast and visual effects [26]. While fugitive theory is infrequently employed in the field of pictorial fusion, this theory has now gained many academics' interest and has obtained good fusion findings.…”

Section: Introductionmentioning

confidence: 99%

Improvement of the medical fusion process of images by fuzzy entropy and transformation of the contourlet

Khazal

Alhaleem

2022

IJEECS

View full text Add to dashboard Cite

<span>Many types of medical pictures have to be fused, as single-modality medical images can give limited information because of the imagery and the complicated architecture of the human organ. This study proposes to offer a platform on which to make clinical diagnoses and to increase the accuracy of the target identification and the quality of the fused pictures by combining the benefits of nonsubsampled contourlet transform (NSCT) and fuzzy entropy. A picture is first broken down into low frequency or high frequency subbands through NSCT. In line with the various features of the low and high frequency components the respective fusion rules must be implemented. It calculates the level of membership of low frequency coefficients. The fusion of coefficients is also calculated and then utilized to retain picture features. By increasing regional energy, high-frequency components are merged. Inverse transformation produces the final fused picture. Experimental results have shown that, based on subjective visual effect and objective assessment standards, the suggested technique produces a satisfactory fusion effect. This process may also achieve high average gradient, standard deviation (SD), and edge preservation and maintain the fused picture features well. Effective reference can be provided by the outcome of the suggested algorithm for patients' assessment.</span>

show abstract

Section: Introductionmentioning

confidence: 99%