Telescope images of astronomical objects and man-made satellites are frequently characterized by high dynamic range and low SNR. We consider the problem of how to enhance these images, with the aim of making them 'visually useful rather than radiometrically accurate. Standard contrast and histogram adjustment tends to strongly amplify noise in dark regions of the image. Sophisticated techniques have been developed to address this problem in the context of natural scenes. However, these techniques often misbehave when confronted with low-SNR scenes that are also mostly empty space. We compare two classes of algorithms: contrast-limited adaptive histogram equalization, which achieves spatial localization via a tiling of the image, and gradient-domain techniques, which perform localized contrast adjustment by non-linearly remapping the gradient of the image in a content-dependent manner. We extend these to include a priori knowledge of SNR and the processing (e.g. deconvolution) that was applied in the preparation of the image. The methods will be illustrated with images of satellites from a ground-based telescope.
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