Image Enhancement Based on Histogram Equalization with Linear Perception Neural Network Method

Arpit Namdev; Neera Lal

Arpit Namdev University Institute of Technology, RGPV, Bhopal, India
Neera Lal University Institute of Technology, RGPV, Bhopal, India

Keywords: Contrast Enhancement, Histogram Equalisation, Brightness Preserving, AMBE, PSNR, LPNNM

Abstract

Image enhancement poses a formidable challenge in low-level image processing. While various strategies, such as histogram equalisation, multipoint histogram equalisations, and picture element-dependent contrast preservation, have been employed, the efficacy of these approaches has not consistently met expectations. In response, this paper proposes a novel image enhancement method based on a linear perception neural network, demonstrating superior results in contrast improvement with brightness preservation. The proposed method leverages the interdependence of image components through a linear perceptron network, incorporating curvelet transform for image transformation into a multi-resolution mode. This transformative approach identifies component differences in picture elements, establishing a dependency characteristic matrix as a weight vector for the perceptron network. The perceptron network dynamically adjusts the weights of input image values, enhancing contrast while preserving brightness. Extensive testing of the image interdependence linear perception neural network method for contrast improvement has been conducted on multiple images. To quantify brightness preservation, comparative analysis with existing image enhancement strategies, such as histogram equalisation, was performed using Absolute Mean Brightness Error (AMBE) metrics. A smaller AMBE value indicates better preservation, while the Peak signal-to-noise ratio (PSNR) was employed to measure contrast improvement, with higher PSNR values indicating superior results. The proposed method (LPNNM) was rigorously evaluated against the conventional histogram equalisation (HE) technique for image enhancement. The results demonstrated that the LPNNM method outperforms HE in terms of both brightness preservation (as indicated by AMBE) and contrast improvement (as indicated by PSNR). This research contributes a robust and effective solution to the challenge of image enhancement, offering a more advanced alternative to existing methodologies.

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References

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