11,279 research outputs found
Noise Power Spectrum Scene-Dependency in Simulated Image Capture Systems
The Noise Power Spectrum (NPS) is a standard measure for image capture system noise. It is derived traditionally from captured uniform luminance patches that are unrepresentative of pictorial scene signals. Many contemporary capture systems apply non- linear content-aware signal processing, which renders their noise scene-dependent. For scene-dependent systems, measuring the NPS with respect to uniform patch signals fails to characterize with accuracy: i) system noise concerning a given input scene, ii) the average system noise power in real-world applications. The scene- and-process-dependent NPS (SPD-NPS) framework addresses these limitations by measuring temporally varying system noise with respect to any given input signal. In this paper, we examine the scene-dependency of simulated camera pipelines in-depth by deriving SPD-NPSs from fifty test scenes. The pipelines apply either linear or non-linear denoising and sharpening, tuned to optimize output image quality at various opacity levels and exposures. Further, we present the integrated area under the mean of SPD-NPS curves over a representative scene set as an objective system noise metric, and their relative standard deviation area (RSDA) as a metric for system noise scene-dependency. We close by discussing how these metrics can also be computed using scene-and-process- dependent Modulation Transfer Functions (SPD-MTF)
Perturbation of the Eigenvectors of the Graph Laplacian: Application to Image Denoising
The original contributions of this paper are twofold: a new understanding of
the influence of noise on the eigenvectors of the graph Laplacian of a set of
image patches, and an algorithm to estimate a denoised set of patches from a
noisy image. The algorithm relies on the following two observations: (1) the
low-index eigenvectors of the diffusion, or graph Laplacian, operators are very
robust to random perturbations of the weights and random changes in the
connections of the patch-graph; and (2) patches extracted from smooth regions
of the image are organized along smooth low-dimensional structures in the
patch-set, and therefore can be reconstructed with few eigenvectors.
Experiments demonstrate that our denoising algorithm outperforms the denoising
gold-standards
Neural Nearest Neighbors Networks
Non-local methods exploiting the self-similarity of natural signals have been
well studied, for example in image analysis and restoration. Existing
approaches, however, rely on k-nearest neighbors (KNN) matching in a fixed
feature space. The main hurdle in optimizing this feature space w.r.t.
application performance is the non-differentiability of the KNN selection rule.
To overcome this, we propose a continuous deterministic relaxation of KNN
selection that maintains differentiability w.r.t. pairwise distances, but
retains the original KNN as the limit of a temperature parameter approaching
zero. To exploit our relaxation, we propose the neural nearest neighbors block
(N3 block), a novel non-local processing layer that leverages the principle of
self-similarity and can be used as building block in modern neural network
architectures. We show its effectiveness for the set reasoning task of
correspondence classification as well as for image restoration, including image
denoising and single image super-resolution, where we outperform strong
convolutional neural network (CNN) baselines and recent non-local models that
rely on KNN selection in hand-chosen features spaces.Comment: to appear at NIPS*2018, code available at
https://github.com/visinf/n3net
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