Adaptive histogram equalization of wavelet sub bands for the enhancement of contrast in aerial images

Abstract Remote sensing images, like satellite images, have played a key role in various sectors of human life. Enhancement of contrast is critical for improved color vision and reproduction. Discrete Wavelet Transform (DWT) and adaptive histogram equalization (AHE) are used in this work to propose a unique method. For images considered for remote sensing, the current methods employ dominant illumination level reasoning and adaptive brightness translation. Using the low-frequency illumination element in wavelets, this technique calculates luminance strength transfer functions and converts pixel intensity as per the transfer function. We use the log-average brightness to divide the sub band (LL) into low, medium, and high frequency layers after performing a discrete wavelet transform on the input images. The gamma adjustment function and knee transfer function are incorporated to adaptively estimate intensity transfer functions depending on the prevailing illumination within each layer. The inverse DWT is used to produce the improved picture after the intensity translation. To increase the image's overall quality, the suggested technique employs adaptive histogram equalization. The current method favours LL sub bands over other bands, necessitating boundary softening and image fusion. This may induce a drop in overall quality. AHE is used in the suggested technique to solve this problem. The suggested method outperforms current methodologies in terms of overall contrast and visibility of local features, according to the findings of the experiments. The suggested approach outperforms the current systems in terms of performance.