Optimal Contrast Enhancement for Remote Sensing Images

This paper presents an optimal contrast enhancement approach for remote sensing images based on dominant brightness level analysis and adaptive intensity transformation for remote sensing images. The proposed system first perform discrete wavelet transform (DWT) on the input images and then split the LL sub band into low-, middle-, and high-intensity layers using the logaverage luminance. The knee transfer function and the gamma adjustment function based on the dominant brightness level of each layer are used to compute the adaptive intensity transfer functions. Then a sparse representation technique is added to gain more resolution. After this transformation, the resulting optimally contrast enhanced image is obtained by using the inverse DWT. The various histogram equalization approaches proposed in the literature, degrade the overall quality of image by altering the saturation in lowand high-intensity regions, and also will not give optimal contrast enhancement. The proposed algorithm overcomes this problem by optimally enhancing the contrast and also the resolution of the input image. The proposed algorithm enhances the overall contrast and visibility of local details better than existing techniques and also gives optimal contrast. The proposed method can optimally enhance any low-contrast satellite images and are also suitable for other various imaging devices such as consumer digital cameras, photorealistic 3-D reconstruction systems, and computational cameras. Keywords— Contrast enhancement, discrete wavelet transforms (DWT), Dominant brightness level analysis, Adaptive intensity transfer function, Remote sensing images.