Algorithm

Histogram

Contrast (vision)

Literature

Computer science

Decoding methods

Pattern recognition (psychology)

Image (mathematics)

Gamma correction

Artificial intelligence

Adaptive histogram equalization

Dual (grammatical number)

Histogram equalization

Computer vision

Equalization (audio)

IEEE Access

We propose automatic contrast-limited adaptive histogram equalization (CLAHE) for image contrast enhancement. We automatically set the clip point for CLAHE based on textureness of a block. Also, we introduce dual gamma correction into CLAHE to achieve contrast enhancement while preserving naturalness. First, we redistribute the histogram of the block in CLAHE based on the dynamic range of each block. Second, we perform dual gamma correction to enhance the luminance, especially in dark regions while reducing over-enhancement artifacts. Since automatic CLAHE adaptively enhances contrast in each block while boosting luminance, it is very effective in enhancing dark images and daylight ones with strong dark shadows. Moreover, automatic CLAHE is computationally efficient, i.e., more than 35 frames/s at null null null $1024\times682$ null null null resolution, due to the independent block processing for contrast enhancement. Experimental results demonstrate that automatic CLAHE with dual gamma correction achieves good performance in contrast enhancement and outperforms state-of-the-art methods in terms of visual quality and quantitative measures.

Automatic Contrast-Limited Adaptive Histogram Equalization With Dual Gamma Correction