In this work, we propose a denoising scheme to restore images degraded by CCD noise. The CCD noise model, measured in the space of incident light values (light space), is a combination of signal-independent and signal-dependent noise terms. This model becomes more complex in image brightness space (normal camera output) due to the nonlinearity of the camera response function that transforms incoming data from light space to image space. We develop two adaptive restoration techniques, both accounting for this nonlinearity. One operates in light space, where the relationship between the incident light and light space values is linear, while the second method uses the transformed noise model to operate in image space. Both techniques apply multiple adaptive filters and merge their outputs to give the final restored image. Experimental results suggest that light space denoising is more efficient, since it enables the design of a simpler filter implementation. Results are given for real images with synthetic noise added, and for images with real noise.

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Given estimates of the motion eld optic ow from an image sequence, it is possible to recover translational direction,T , using a variety of techniques. One such technique, known as subspace methods," generates constraints which are p erpendicular toT , s o that two distinct constraints allow a solution forT . I n practice many constraints are used i n a l e ast-squares solution, but it has been observed that the recovered estimates forT are biased towards the optical axis. While the cause of the bias is well known, previous attempts to remove it have been awed. This paper outlines a new method which removes the bias. The technique is simple to apply and computationally ecient.

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