.png){kind=icon}
.png){kind=icon}
Return
Context-Based Novel Histogram Bin Stretching Algorithm for Automatic Contrast Enhancement
.png){kind=icon}
PRE
AI
DOI：10.1145/3597303.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This article presents CHBS, a novel context-based histogram bin stretching method that enhances the contrast by increasing the range of gray levels and randomness among the gray levels. It comprises image spatial contextual information and discrete cosine transform (DCT). It constitutes the global enhancement with the context-based histogram bin stretching and local details with the DCT. First, it uses the spatial similarities among surrounding pixels to generate random numbers. Unlike the other methods, the similarity map is generated based on the neighboring pixels' mutual relationship. Intensity values are distributed among the available dynamic range to generate a global contrast-enhanced image. Second, the DCT is further applied to the previous contrast-enhanced image to adjust its local details automatically. Several experiments are conducted on the different levels of contrast degraded images. Both subjective and objective assessment outcomes validate that the projected approach is better or comparable with several state-of-the-art approaches in terms of brightness preservation, richer details, and natural appearance.
Keywords:
Histogram bin stretching
contrast enhancement
spatial contextual information
discrete cosine transform
Journal
IF:
6
Papers:
2.0K
Citations:
5.4K