Slantlet filter-bank-based satellite image enhancement using gamma-corrected knee transformation

ABSTRACT In this paper, an efficient and relatively fast approach for satellite image enhancement is proposed. This technique is based on auto-knee transfer function with suitable gamma correction using slantlet transform for two-scale decomposed image. Dark or low contrast, big-data (or large sized) multispectral images can be easily enhanced by proper tuning for the value of gamma-parameter using slantlet transform. Here, sub-band decomposition is achieved by employing single-level slantlet filter-bank, which is just equivalent to second-level sub-band decomposition using discrete wavelet transform) that has been employed initially. For this purpose, main information of the image is concentrated to lowest sub-band, over which gamma correction is applied after computing the knee transfer function adaptively for low quality input image. In addition to this two-scale decomposition-based enhancement, here, gamma-corrected energy redistributed slantlet transform-based textural enhancement framework is also suggested. The experimentation comprised of relative performance evaluation and comparison on the same scale; clearly reflects the outperformance of proposed methodology over various well-known pre-existing state-of-the-art techniques both quantitatively and qualitatively.