Algorithm

Histogram

Pixel

Engineering

Color image

Contrast (vision)

Image processing

Joint (building)

Computer science

Histogram matching

Decoding methods

Pattern recognition (psychology)

Image (mathematics)

Artificial intelligence

Image histogram

Adaptive histogram equalization

Architectural engineering

Balanced histogram thresholding

Noise (video)

Histogram equalization

Computer vision

Equalization (audio)

Journal of King Saud University - Computer and Information Sciences

The limitation to the most commonly used histogram equalization (HE) technique is the inconsideration of the neighborhood info near each pixel for contrast enhancement. This gives rise to noise in the output image. To overcome this effect, a novel joint histogram equalization (JHE) based technique is suggested. The focus is to utilize the information among each pixel and its neighbors, which improves the contrast of an image. The suggested method is developed in a truly two-dimensional domain. The joint histogram is constructed using the original image and its average image. Further, it does not require a target uniform distribution for generating the output. The two-dimensional cumulative distribution function (CDF) is utilized as a mapping function to get the output pixel intensity. Extensive experiments are performed using 300 test images from BSD database. The experimental analysis indicates that the procedure produces better results than the state-of-the-art HE based contrast enhancement algorithms. More significantly, it produces the best results even for images having a narrow dynamic range. The implementation simplicity of the proposed algorithm may attract researchers to explore the idea for new applications in image processing.

A novel joint histogram equalization based image contrast enhancement