In the past few years, supervised networks have achieved promising performance on image denoising. These methods learn image priors and synthetic noise statistics from plenty pairs of noisy and clean images. Recently, several unsupervised denoising networks are proposed only using external noisy images for training. However, the networks learned from external data inherently suffer from the domain gap dilemma, i.e., the image priors and noise statistics are very different between the training data and the corrupted test images. This dilemma becomes more clear when dealing with the signal dependent realistic noise in real photographs. In this work, we provide a statistically useful conclusion: it is possible to learn an unsupervised network only with the corrupted image, approximating the optimal parameters of a supervised network learned with pairs of noisy and clean images. This is achieved by proposing a "Noisy-As-Clean" strategy: taking the corrupted image as "clean" target and the simulated noisy images (based on the corrupted image) as inputs. Extensive experiments show that the unsupervised denoising networks learned with our "Noisy-As-Clean" strategy surprisingly outperforms previous supervised networks on removing several typical synthetic noise and realistic noise. The code will be publicly released.
are widely used for different image restoration tasks @cite_46 @cite_53 @cite_15 @cite_43 @cite_54 @cite_0 . They can be divided into internal priors and external priors. The internal image priors are directly learned on the input test image itself, while the external image priors need to be learned with external images (as long as not the test input). The internal priors are adaptive to its contents, but somewhat affected by the corruptions @cite_46 @cite_53 @cite_15 . By contrast, the external priors are effective for restoring general images, but not optimal for specific test image @cite_43 @cite_54 @cite_0 .
@cite_15 @cite_54 @cite_53 @cite_0 @cite_43 @cite_46
2951811868 2116645175 2203654268 2172275395
We propose to leverage denoising autoencoder networks as priors to address image restoration problems. We build on the key observation that the output of an optimal denoising autoencoder is a local mean of the true data density, and the autoencoder error (the difference between the output and input of the trained autoencoder) is a mean shift vector. We use the magnitude of this mean shift vector, that is, the distance to the local mean, as the negative log likelihood of our natural image prior. For image restoration, we maximize the likelihood using gradient descent by backpropagating the autoencoder error. A key advantage of our approach is that we do not need to train separate networks for different image restoration tasks, such as non-blind deconvolution with different kernels, or super-resolution at different magnification factors. We demonstrate state of the art results for non-blind deconvolution and super-resolution using the same autoencoding prior. We present an image restoration method that leverages a large database of images gathered from the web. Given an input image, we execute an efficient visual search to find the closest images in the database; these images define the input's visual context. We use the visual context as an image-specific prior and show its value in a variety of image restoration operations, including white balance correction, exposure correction, and contrast enhancement. We evaluate our approach using a database of 1 million images downloaded from Flickr and demonstrate the effect of database size on performance. Our results show that priors based on the visual context consistently out-perform generic or even domain-specific priors for these operations. Natural image modeling plays a key role in many vision problems such as image denoising. Image priors are widely used to regularize the denoising process, which is an illposed inverse problem. One category of denoising methods exploit the priors (e.g., TV, sparsity) learned from external clean images to reconstruct the given noisy image, while another category of methods exploit the internal prior (e.g., self-similarity) to reconstruct the latent image. Though the internal prior based methods have achieved impressive denoising results, the improvement of visual quality will become very difficult with the increase of noise level. In this paper, we propose to exploit image external patch prior and internal self-similarity prior jointly, and develop an external patch prior guided internal clustering algorithm for image denoising. It is known that natural image patches form multiple subspaces. By utilizing Gaussian mixture models (GMMs) learning, image similar patches can be clustered and the subspaces can be learned. The learned GMMs from clean images are then used to guide the clustering of noisypatches of the input noisy images, followed by a low-rank approximation process to estimate the latent subspace for image recovery. Numerical experiments show that the proposed method outperforms many state-of-the-art denoising algorithms such as BM3D and WNNM. Learning good image priors is of utmost importance for the study of vision, computer vision and image processing applications. Learning priors and optimizing over whole images can lead to tremendous computational challenges. In contrast, when we work with small image patches, it is possible to learn priors and perform patch restoration very efficiently. This raises three questions - do priors that give high likelihood to the data also lead to good performance in restoration? Can we use such patch based priors to restore a full image? Can we learn better patch priors? In this work we answer these questions. We compare the likelihood of several patch models and show that priors that give high likelihood to data perform better in patch restoration. Motivated by this result, we propose a generic framework which allows for whole image restoration using any patch based prior for which a MAP (or approximate MAP) estimate can be calculated. We show how to derive an appropriate cost function, how to optimize it and how to use it to restore whole images. Finally, we present a generic, surprisingly simple Gaussian Mixture prior, learned from a set of natural images. When used with the proposed framework, this Gaussian Mixture Model outperforms all other generic prior methods for image denoising, deblurring and inpainting.