Natural Image Noise Dataset

Convolutional neural networks have been the focus of research aiming to solve image denoising problems, but their performance remains unsatisfactory for most applications. These networks are trained with synthetic noise distributions that do not accurately reflect the noise captured by image sensors. Some datasets of clean-noisy image pairs have been introduced but they are usually meant for benchmarking or specific applications. We introduce the Natural Image Noise Dataset (NIND), a dataset of DSLR-like images with varying levels of ISO noise which is large enough to train models for blind denoising over a wide range of noise. We demonstrate a denoising model trained with the NIND and show that it significantly outperforms BM3D on ISO noise from unseen images, even when generalizing to images from a different type of camera. The Natural Image Noise Dataset is published on Wikimedia Commons such that it remains open for curation and contributions. We expect that this dataset will prove useful for future image denoising applications.Comment: NTIRE at CVPR 201

On the Importance of Denoising when Learning to Compress Images

Image noise is ubiquitous in photography. However, image noise is not compressible nor desirable, thus attempting to convey the noise in compressed image bitstreams yields sub-par results in both rate and distortion. We propose to explicitly learn the image denoising task when training a codec. Therefore, we leverage the Natural Image Noise Dataset, which offers a wide variety of scenes captured with various ISO numbers, leading to different noise levels, including insignificant ones. Given this training set, we supervise the codec with noisy-clean image pairs, and show that a single model trained based on a mixture of images with variable noise levels appears to yield best-in-class results with both noisy and clean images, achieving better rate-distortion than a compression-only model or even than a pair of denoising-then-compression models with almost one order of magnitude fewer GMac operations

End-to-end optimized image compression with competition of prior distributions

Convolutional autoencoders are now at the forefront of image compression research. To improve their entropy coding, encoder output is typically analyzed with a second autoencoder to generate per-variable parametrized prior probability distributions. We instead propose a compression scheme that uses a single convolutional autoencoder and multiple learned prior distributions working as a competition of experts. Trained prior distributions are stored in a static table of cumulative distribution functions. During inference, this table is used by an entropy coder as a look-up-table to determine the best prior for each spatial location. Our method offers rate-distortion performance comparable to that obtained with a predicted parametrized prior with only a fraction of its entropy coding and decoding complexity

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Desorption kinetics of Cd, Zn and Ni measured in intact soils by DGT.

DGT (diffusive gradients in thin films) was used to measure the distribution and rates of exchange of Zn, Cd, and Ni between solid phase and solution in five different soils. Soil texture ranged from sandy loam to clay, pH ranged from 4.9 to 7.1, and organic carbon content ranged from 0.8% to 5.8%. DGT devices continuously remove metal to a Chelex gel layer after passage through a well-defined diffusion layer. The magnitude of the induced remobilization flux from the solid phase is related to the pool size of labile metal and the exchange kinetics between dissolved and sorbed metal. DGT devices were deployed over a series of times (4 h to 3 weeks), and the DIFS model (DGT induced fluxes in soils) was used to derive distribution coefficients for labile metal (Kdl) and the rate at which the soil system can supply metal from solid phase to solution, expressed as a response time. Response times for Zn and Cd were short generally (<8 min). They were so short in some soils (<1 min) that no distinction could be made between supply of metal being controlled by diffusion or the rate of release. Generally longer response times for Ni (5−20 min) were consistent with its slow desorption. The major factor influencing Kdl for Zn and Cd was pH, but association with humic substances in the solid phase also appeared to be important. The systematic decline, with increasing pH, in both the pool size of Ni available to the DGT device and the rate constant for its release is consistent with a part of the soil Ni pool being unavailable within a time scale of 1−20 min. This kinetic limitation is likely to limit the availability of Ni to plants