Satellite image enhancement using beta wavelet based gamma corrected adaptive knee transformation

In this paper, the gamma corrected adaptive knee transformation, proposed by Monobe et al. is reviewed and improved by exploiting two-dimensional single level Beta wavelet filter for subband decomposition of the low contrast satellite images. For this purpose, the input image is initially decomposed into its subband images such as low-low (LL), low-high (LH), high-low (HL), and high-high (HH)). Then, LL-band coefficients of the input image are transformed to obtain enhanced LL-band by applying gamma corrected adaptive knee transformation. Finally, it is combined with rest of the subbands for desired alias free and distortion less reconstruction of the enhanced image. A comparative study of different wavelet filters has been performed and it is evident that Beta function and its derivatives based wavelet filter outperforms over other wavelet filters.