In daylight viewing conditions, image contrast is often significantly degraded by atmospheric aerosols such as haze and fog. This paper introduces a method for reducing this degradation in situations in which the scene geometry is known. Contrast is lost because light is scattered toward the sensor by the aerosol particles and because the light reflected by the terrain is attenuated by the aerosol. This degradation is approximately characterized by a simple, physically based model with three parameters. The method involves two steps: first, an inverse problem is solved in order to recover the three model parameters; then, for each pixel, the relative contributions of scattered and reflected flux are estimated. The estimated scatter contribution is simply subtracted from the pixel value and the remainder is scaled to compensate for aerosol attenuation. This paper describes the image processing algorithm and presents an analysis of the signal-to-noise ratio (SNR) in the resulting enhanced image. This analysis shows that the SNR decreases exponentially with range. A temporal filter structure is proposed to solve this problem. Results are presented for two image sequences taken from an airborne camera in hazy conditions and one sequence in clear conditions. A satisfactory agreement between the model and the experimental data is shown for the haze conditions. A significant improvement in image quality is demonstrated when using the contrast enhancement algorithm in conjuction with a temporal filter.

A new method for reconstructing a compact pixellated image from the zeros of its z-transform is presented. Particular zeros are chosen from the infinity which form the zero sheet of the z-transform in order to construct samples (spaced according to the Nyquist criterion) in complex Fourier space. Inverse discrete Fourier transformation and division by a known exponential weighting function reconstructs the image to within an arbitrary multiplicative complex constant. The method allows greater flexibility in the use of the zero sheet in deconvolution and phase retrieval than is possible with methods which use only the Fourier components at real spatial frequencies. Images as large as 64/spl times/64 pixels have been successfully reconstructed using the technique; it is equally applicable to both real and complex images. The computational complexity of the method is O(N/sup 3/).>