Automatic Denoising Parameter Estimation Using Gradient Histograms

State-of-the-art denoising methods provide denoising results that can be considered close to optimal. The denoising methods usually have one or more parameters regulating denoising strength that can be adapted for a specific image. To obtain the optimal denoising result, the correct parameter setting is crucial. In this paper, we therefore propose a method that can automatically estimate the optimal parameter of a denoising algorithm. Our approach compares the gradient histogram of a denoised image to an estimated reference gradient histogram. The reference gradient histogram is estimated based on down- and upsampling of the noisy image, thus our method works without a reference and is image-adaptive. We evaluate our propsed down-/upsampling-based gradient histogram method (DUG) based on a subjective test with 20 participants. In the test data, we included images from both the Kodak data set and the more realistic ARRI data set and we used the state-of-the-art denoising method BM3D. Based on the test results we can show that the parameter estimated by our method is very close to the human perception. Despite being very fast and simple to implement, our method shows a lower error than all other suitable no-reference metrics we found.     «

State-of-the-art denoising methods provide denoising results that can be considered close to optimal. The denoising methods usually have one or more parameters regulating denoising strength that can be adapted for a specific image. To obtain the optimal denoising result, the correct parameter setting is crucial. In this paper, we therefore propose a method that can automatically estimate the optimal parameter of a denoising algorithm. Our approach compares the gradient histogram of a denoised im...     »