Noise suppression approach with the BV-L1 nonlinear image decomposition

In a digital camera, several factors cause signal-dependency of additive noise. Many denoising methods have been proposed, but many of them do not necessarily work well for actual signal-dependent noise. To solve the problem of removing signal-dependent noise of a digital color camera, this paper presents a denoising approach via nonlinear image-decomposition. As a pre-process, we employ the BV-L1 nonlinear image-decomposition variational model. This variational model decomposes an input image into three components: a structural component corresponding to a cartoon image approximation collecting geometrical image features, a texture component corresponding to fine image textures, and a residual component. Each separated component is denoised with a denoising method suitable to it. For an image taken with a digital color camera under the condition of high ISO sensitivity, the BV-L1 model removes its signal-dependent noise to a large extent from its separated structural component, in which geometrical image features are well preserved, but the structural component sometimes suffers color-smear artifacts. To remove those color-smear artifacts, we apply the sparse 3D transform-domain collaborative filtering to the separated structural component. On the other hand, the texture component and the residual component are rather contaminated with noise, and the effects of noise are selectively removed from them with our proposed color shrinkage denoising schemes utilizing inter-channel color crosscorrelations. Our method achieves efficient denoising and selectively removes signal-dependent noise of a digital color camera.