36,425 research outputs found
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation
In this paper, we present new data pre-processing and augmentation techniques
for DNN-based raw image denoising. Compared with traditional RGB image
denoising, performing this task on direct camera sensor readings presents new
challenges such as how to effectively handle various Bayer patterns from
different data sources, and subsequently how to perform valid data augmentation
with raw images. To address the first problem, we propose a Bayer pattern
unification (BayerUnify) method to unify different Bayer patterns. This allows
us to fully utilize a heterogeneous dataset to train a single denoising model
instead of training one model for each pattern. Furthermore, while it is
essential to augment the dataset to improve model generalization and
performance, we discovered that it is error-prone to modify raw images by
adapting augmentation methods designed for RGB images. Towards this end, we
present a Bayer preserving augmentation (BayerAug) method as an effective
approach for raw image augmentation. Combining these data processing technqiues
with a modified U-Net, our method achieves a PSNR of 52.11 and a SSIM of 0.9969
in NTIRE 2019 Real Image Denoising Challenge, demonstrating the
state-of-the-art performance. Our code is available at
https://github.com/Jiaming-Liu/BayerUnifyAug.Comment: Accepted by CVPRW 201
Adaptive Image Denoising by Targeted Databases
We propose a data-dependent denoising procedure to restore noisy images.
Different from existing denoising algorithms which search for patches from
either the noisy image or a generic database, the new algorithm finds patches
from a database that contains only relevant patches. We formulate the denoising
problem as an optimal filter design problem and make two contributions. First,
we determine the basis function of the denoising filter by solving a group
sparsity minimization problem. The optimization formulation generalizes
existing denoising algorithms and offers systematic analysis of the
performance. Improvement methods are proposed to enhance the patch search
process. Second, we determine the spectral coefficients of the denoising filter
by considering a localized Bayesian prior. The localized prior leverages the
similarity of the targeted database, alleviates the intensive Bayesian
computation, and links the new method to the classical linear minimum mean
squared error estimation. We demonstrate applications of the proposed method in
a variety of scenarios, including text images, multiview images and face
images. Experimental results show the superiority of the new algorithm over
existing methods.Comment: 15 pages, 13 figures, 2 tables, journa
Wavelet based joint denoising of depth and luminance images
In this paper we present a new method for joint denoising of depth and luminance images produced by time-of-flight camera. Here we assume that the sequence does not contain outlier points which can be present in the depth images. Our method first performs estimation of noise and signal covariance matrices and then performs vector denoising. Two versions of the algorithm are presented, depending on the method used for the classification of the image contexts. Denoising results are compared with the ground truth images obtained by averaging of the multiple frames of the still scene
Content adaptive wavelet based method for joint denoising of depth and luminance images
In this paper we present a new method for joint denoising of depth and luminance images produced by time-of-flight camera. Here we assume that the sequence does not contain outlier points which can be present in the depth images. Our method first performs estimation of noise and signal covariance matrices and then performs vector denoising. Luminance image is segmented into similar contexts usina k-means algorithm, which are used for calculation of covariance matrices. Denoising results are compared with the ground truth images obtained by averaging of the multiple frames of the still scene
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