In a digital camera, several factors cause signal-dependency of additive noise. Many denoising methods have been
proposed, but unfortunately most of them do not work well for the actual signal-dependent noise. To solve the problem
of removing the signal-dependent noise of a digital camera, we present a denoising approach via the nonlinear imagedecomposition.
In the nonlinear decomposition-and-denoising approach, at the first nonlinear image-decomposition
stage, multiplicative image-decomposition is performed, and a noisy image is represented as a product of its two
components so that its structural component corresponding to a cartoon approximation of the noisy image may not be
corrupted by the noise and its texture component may collect almost all the noise. At the successive nonlinear denoising
stage, intensity of the separated structural component is utilized instead of the unknown true signal value, to adapt the
soft-thresholding-type denoising manipulation of the texture component to the signal dependency of the noise. At the
final image-synthesis stage, the separated structure component is combined with the denoised texture component, and
thus a sharpness-improved denoised image is reproduced. The nonlinear decomposition-and-denoising approach can
selectively remove the signal-dependent noise of a digital camera without not only blurring sharp edges but also
destroying visually important textures.