Multiobjectives bihistogram equalization for image contrast enhancement

Abstract

The global histogram equalization (HE) has been the most frequently adopted image contrast enhancement technique. A brightness and detail‐preserving HE method with good contrast enhancement effect has been a goal of much recent research in HE. Nevertheless, producing a well‐balanced HE is deemed to be a daunting task. In this article, we propose a novel framework of HE with the aim of taking three desirable properties into account: brightness preservation, detail preservation, and contrast enhancement. We termed the proposed method as multipurpose beta optimized bi‐HE (MBOBHE). MBOBHE consists of performing the histogram optimization separately in both subhistograms after the segmentation of histogram using an optimized separating point based on the three performance criteria using a weighted‐sum aggregated objective function (AOF). Both quantitative and qualitative results indicate that MBOBHE outperforms other existing bi‐HE methods, in terms of comprehensive performance of HE that is capable of providing a holistic view. © 2014 Wiley Periodicals, Inc. Complexity 20: 22–36, 2014

Connecting Fluvial Levee Deposition to Flood‐Basin Hydrology

“…Next, we isolate near‐channel topography in the LiDAR data within three channel widths along both banks using a histogram equalization method. This method is often used by the medical industry to analyze X‐rays because the data distribution is reshaped to create a linear cumulative distribution function, which has the distinct advantage of enhancing low contrast areas (Hum et al, ). In the context of a DEM, this enhances the low relief of the levee toe where slopes are small (Figure ).…”

Connecting fluvial levee deposition to flood-basin hydrology

“…Next, we isolate near channel topography in the LiDAR data within three channel widths along both banks using a histogram equalization method. This method is often used by the medical industry to analyze x-rays because the data distribution is reshaped to create a linear cumulative distribution function, which has the distinct advantage of enhancing low contrast areas [Hum et al, 2014]. In the context of a DEM, this enhances the low relief of the levee toe where slopes are small ( Figure 3).…”

Comportamiento Temporal Y Espacial Del Bosque Ribereño en El Curso Bajo Del Río Portoviejo Y La Quebrada Chilán, Provincia De Manabí, Ecuador

“…El método usado en esta ocasión fue el conocido como el del histograma mínimo. De acuerdo con Hum et al (2014), este método se limita a substraer en cada banda el valor mínimo observado, ya que se supone que en una escena siempre pueden existir algunos pixeles en sombra total, que en ausencia de atmósfera no recibirían ni deberían reflejar ninguna energía de procedencia solar. Sin embargo, a objeto de hacer mejoras se consideró la metodología empleada por Song et al (2001) y por Brizuela et al (2007), para obtener el valor de dispersión para el infrarrojo medio y calcular el valor teórico de dispersión para el resto de las longitudes de onda.…”