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Efficient coding models predict that the optimal code for natural images is a population of oriented Gabor receptive fields. These results match response properties of neurons in primary visual cortex, but not those in the retina. Does the retina use an optimal code, and if so, what is it optimized for? Previous theories of retinal coding have assumed that the goal is to encode the maximal amount of information about the sensory signal. However, the image sampled by retinal photoreceptors is degraded both by the optics of the eye and by the photoreceptor noise. Therefore, de-blurring and de-noising of the retinal signal should be important aspects of retinal coding. Furthermore, the ideal retinal code should be robust to neural noise and make optimal use of all available neurons. Here we present a theoretical framework to derive codes that simultaneously satisfy all of these desiderata. When optimized for natural images, the model yields filters that show strong similarities to retinal ganglion cell (RGC) receptive fields. Importantly, the characteristics of receptive fields vary with retinal eccentricities where the optical blur and the number of RGCs are significantly different. The proposed model provides a unified account of retinal coding, and more generally, it may be viewed as an extension of the Wiener filter with an arbitrary number of noisy units.