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Burst Denoising with Kernel Prediction Networks

Ben Mildenhall, Jonathan T. Barron, Jiawen Chen, Dillon Sharlet, Ren Ng, Robert Carroll
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
In particular, we propose a convolutional neural network architecture for predicting spatially varying kernels that can both align and denoise frames, a synthetic data generation approach based on a realistic  ...  We present a technique for jointly denoising bursts of images taken from a handheld camera.  ...  Predicted kernels Our network predicts a stack of 2D kernels at each pixel which we visualize in Fig. 7 .  ... 
doi:10.1109/cvpr.2018.00265 dblp:conf/cvpr/MildenhallBCSNC18 fatcat:pkz7emcrujfpvej5ifdyzklrcm

Burst Denoising with Kernel Prediction Networks [article]

Ben Mildenhall, Jonathan T. Barron, Jiawen Chen, Dillon Sharlet, Ren Ng, Robert Carroll
2018 arXiv   pre-print
In particular, we propose a convolutional neural network architecture for predicting spatially varying kernels that can both align and denoise frames, a synthetic data generation approach based on a realistic  ...  We present a technique for jointly denoising bursts of images taken from a handheld camera.  ...  Predicted kernels Our network predicts a stack of 2D kernels at each pixel which we visualize in Fig. 7 .  ... 
arXiv:1712.02327v2 fatcat:wvjxs7ysbbdqbaq3idmtiqxgz4

Basis Prediction Networks for Effective Burst Denoising with Large Kernels [article]

Zhihao Xia, Federico Perazzi, Michaël Gharbi, Kalyan Sunkavalli, Ayan Chakrabarti
2020 arXiv   pre-print
To this end, we introduce a novel basis prediction network that, given an input burst, predicts a set of global basis kernels -- shared within the image -- and the corresponding mixing coefficients --  ...  This allows us to effectively exploit comparatively larger denoising kernels, achieving both significant quality improvements (over 1dB PSNR) and faster run-times over state-of-the-art methods.  ...  ). input burst using a "kernel prediction network" (KPN).  ... 
arXiv:1912.04421v2 fatcat:3symba4cpzb6dkeyfbluhth3iq

Multi-Kernel Prediction Networks for Denoising of Burst Images [article]

Talmaj Marinč, Vignesh Srinivasan, Serhan Gül, Cornelius Hellge, Wojciech Samek
2019 arXiv   pre-print
We propose a deep neural network based approach called Multi-Kernel Prediction Networks (MKPN) for burst image denoising.  ...  Recent approaches for image denoising aim to predict kernels which are convolved with a set of successively taken images (burst) to obtain a clear image.  ...  Given a noisy set of input burst images, kernels are predicted for each pixel using a deep neural network.  ... 
arXiv:1902.05392v1 fatcat:2k3kvawaorbgpg3tyevfsdhszy

Attention Mechanism Enhanced Kernel Prediction Networks for Denoising of Burst Images [article]

Bin Zhang, Shenyao Jin, Yili Xia, Yongming Huang, Zixiang Xiong
2020 arXiv   pre-print
In this paper, attention mechanism enhanced kernel prediction networks (AME-KPNs) are proposed for burst image denoising, in which, nearly cost-free attention modules are adopted to first refine the feature  ...  Simulations and real-world experiments are conducted to illustrate the robustness of the proposed AME-KPNs in burst image denoising.  ...  (c) (d) (e) (f) CONCLUSION Novel attention mechanism enhanced kernel prediction networks (AME-KPNs) have been proposed for burst image denoising.  ... 
arXiv:1910.08313v2 fatcat:66ppnqgb6ncgdoawgauz7sl4hi

Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light Environments [article]

Zhihao Xia, Michaël Gharbi, Federico Perazzi, Kalyan Sunkavalli, Ayan Chakrabarti
2021 arXiv   pre-print
We introduce a neural network-based method to denoise pairs of images taken in quick succession, with and without a flash, in low-light environments.  ...  Our network outputs a gain map and a field of kernels, the latter obtained by linearly mixing elements of a per-image low-rank kernel basis.  ...  Here, our network predicts kernels to be used to filter and sum all the no-flash images, which is then multiplied with our scale map.  ... 
arXiv:2012.05116v2 fatcat:iag6usixvrcfddflsxp5es2xwq

Digital Gimbal: End-to-end Deep Image Stabilization with Learnable Exposure Times [article]

Omer Dahary, Matan Jacoby, Alex M. Bronstein
2021 arXiv   pre-print
We demonstrate this method's advantage over the traditional approach of deblurring a single image or denoising a fixed-exposure burst on both synthetic and real data.  ...  To exploit the trade-off between motion blur at long exposures and low SNR at short exposures, we train a CNN that estimates a sharp high-SNR image by aggregating a burst of noisy short-exposure frames  ...  [26] propose an adaptive kernel prediction network for jointly aligning and merging noisy frames.  ... 
arXiv:2012.04515v4 fatcat:vsczuc7ovvcprpn3uhyal3nhnu

Efficient Burst Raw Denoising with Variance Stabilization and Multi-frequency Denoising Network [article]

Dasong Li, Yi Zhang, Ka Lung Law, Xiaogang Wang, Hongwei Qin, Hongsheng Li
2022 arXiv   pre-print
Denoising based on a burst of multiple frames generally outperforms single frame denoising but with the larger compututional cost.  ...  Instead, we resort to a conventional and efficient alignment method and combine it with our multi-frame denoising network.  ...  KPN [36] proposes kernel prediction network to jointly conduct multi-frame alignment and denoising. MPKN [34] extends single kernel prediction to multiple kernels prediction.  ... 
arXiv:2205.04721v1 fatcat:yslxm2bymfgcpc5brv2lhrgexe

Deep Motion Blur Removal Using Noisy/Blurry Image Pairs [article]

Shuang Zhang, Ada Zhen, Robert L. Stevenson
2019 arXiv   pre-print
Recent progress in deep neural networks suggests that kernel free single image deblurring can be efficiently performed, but questions about deblurring performance persist.  ...  Thus, we propose to restore a sharp image by fusing a pair of noisy/blurry images captured in a burst.  ...  Comparison with Burst Denoising Methods Burst image denoising is an alternative strategy to the noisy/blurry image pair deblurring.  ... 
arXiv:1911.08541v2 fatcat:untm7ndhn5ar3bui3hi45jyh5i

Burst Image Restoration and Enhancement [article]

Akshay Dudhane, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan, Ming-Hsuan Yang
2022 arXiv   pre-print
burst low-light image enhancement, and burst denoising tasks.  ...  In comparison to existing works that usually follow a late fusion scheme with single-stage upsampling, our approach performs favorably, delivering state-of-the-art performance on burst superresolution,  ...  [39] generate per-pixel kernels through the kernel prediction network (KPN) to merge the input images.  ... 
arXiv:2110.03680v2 fatcat:6cwkgx2t4fc4jnkz33wu55mhcy

Deep Learning on Image Denoising: An overview [article]

Chunwei Tian, Lunke Fei, Wenxian Zheng, Yong Xu, Wangmeng Zuo, Chia-Wen Lin
2020 arXiv   pre-print
However, there are substantial differences in the various types of deep learning methods dealing with image denoising.  ...  We first classify the deep convolutional neural networks (CNNs) for additive white noisy images; the deep CNNs for real noisy images; the deep CNNs for blind denoising and the deep CNNs for hybrid noisy  ...  kernel idea for burst denoising Zhang et al. (2020) [255] CNN Burst denoising CNN with kernel idea and attention idea for burst denoising Zhao et al. (2019) [269] CNN Burst denoising CNN for burst denoising  ... 
arXiv:1912.13171v4 fatcat:4ts2xpivhreptelbgeqhljjiri

Denoising Real Bursts with Squeeze-and-excitation Residual Network

Hanlin Tan, Huaxin Xiao, Shiming Lai, Yu Liu, Mao-jun Zhang
2020 IET Image Processing  
In this study, the authors propose a deep residual model with squeeze-and-excitation (SE) modules for the burst denoising.  ...  The network contains a noise estimation convolutional neural network, which makes it capable of blind denoising.  ...  Data-driven approaches are also applied to burst denoising. KPN [10] propose a CNN that predicts multiple kernels to convolve with bursts and sum to a clean estimate. Xu et al.  ... 
doi:10.1049/iet-ipr.2020.0041 fatcat:axtfildm4jfx7jpuejzieconf4

Dynamic Low-light Imaging with Quanta Image Sensors [article]

Yiheng Chi, Abhiram Gnanasambandam, Vladlen Koltun, Stanley H. Chan
2020 arXiv   pre-print
We fill the gap by proposing a student-teacher training protocol that transfers knowledge from a motion teacher and a denoising teacher to a student network.  ...  QIS are single-photon image sensors with photon counting capabilities.  ...  Recent reports on burst photography have focused on using deep neural networks [56] - [59] . Among these, the kernel prediction network (KPN) by Mildenhall et al.  ... 
arXiv:2007.08614v1 fatcat:tbtsrkvf4nf3zf7m4ejzigc2ky

Pasadena: Perceptually Aware and Stealthy Adversarial Denoise Attack [article]

Yupeng Cheng, Qing Guo, Felix Juefei-Xu, Wei Feng, Shang-Wei Lin, Weisi Lin, Yang Liu
2021 arXiv   pre-print
Second, we formulate this new task as a kernel prediction problem for image filtering and propose the adversarial-denoising kernel prediction that can produce adversarial-noiseless kernels for effective  ...  ., whether the image denoising can be given the capability of fooling the state-of-the-art deep neural networks (DNNs) while enhancing the image quality.  ...  [13] construct a UNet-based network to predict kernels for handling burst images and achieve impressive denoising performance.  ... 
arXiv:2007.07097v3 fatcat:3pdmqigybvay3a2mqxiz67ri2m

Non-uniform Blur Kernel Estimation via Adaptive Basis Decomposition [article]

Guillermo Carbajal, Patricia Vitoria, Mauricio Delbracio, Pablo Musé, José Lezama
2021 arXiv   pre-print
Given a blurry image, a neural network is trained to estimate a set of image-adaptive basis motion kernels as well as the mixing coefficients at the pixel level, producing a per-pixel motion blur field  ...  When applied to real motion-blurred images, a variational non-uniform blur removal method fed with the estimated blur kernels produces high-quality restored images.  ...  Kernel Prediction Networks Recently, Kernel Prediction Networks (KPN) have been proposed for low-level vision tasks such burst denoising [31, 48] , optical flow estimation, frame interpolation [34, 35  ... 
arXiv:2102.01026v2 fatcat:svnhprizjnfr3jioflfsg3n6nm
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