Which Denoiser? Blind Selection from Noise Structure

Denoising research has produced candidates of increasingly narrow competence, each strong on the noise it was designed or trained for and weak elsewhere. On an arbitrary image, nothing indicates which candidate to run: the scene and the corruption are both unknown, and no clean reference is available. Practitioners therefore apply the denoiser that performs best on average, which in the pools we study falls 1 to 2.5 dB short of the best choice for each image. Closing that gap requires knowing which denoiser suits the image, which is usually approached either by identifying the corruption first or by running every candidate and judging the results. We show that the right denoiser can be found without identifying the corruption exactly, since corruptions that differ often call for the same treatment. In a space of thirteen statistics of the corrupted image, each measuring one physical property of the process that produced the noise, the nearest neighbor of an image has the same best denoiser in 83 to 89% of cases, even though it has the same corruption in only 60%. Matching a new image to its nearest calibration image in this space and running the denoiser that won there recovers 43 to 66% of the available improvement on corruptions withheld from calibration, in one denoising call and with no retraining. Routing falls below the best overall denoiser on noise far from any calibration condition, such as real camera noise, but adding labeled references of that noise restores the gain without retraining. A descriptor space organized by how noise is best treated therefore turns a pool of denoisers with different strengths into a better denoiser than any of its members.