6 code implementations • 4 Apr 2022 • Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, Yinxiao Li
We also show that our proposed model expresses strong generative modeling capability on ImageNet, demonstrating the superior potential of MaxViT blocks as a universal vision module.
Ranked #14 on
Image Classification
on ImageNet
1 code implementation • CVPR 2022 • Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, Yinxiao Li
In this work, we present a multi-axis MLP based architecture called MAXIM, that can serve as an efficient and flexible general-purpose vision backbone for image processing tasks.
Ranked #1 on
Deblurring
on RealBlur-J
(using extra training data)
no code implementations • CVPR 2022 • Jay Whang, Mauricio Delbracio, Hossein Talebi, Chitwan Saharia, Alexandros G. Dimakis, Peyman Milanfar
Unlike existing techniques, we train a stochastic sampler that refines the output of a deterministic predictor and is capable of producing a diverse set of plausible reconstructions for a given input.
no code implementations • 15 Oct 2021 • Pascal Tom Getreuer, Peyman Milanfar, Xiyang Luo
Partial differential equations (PDEs) are typically used as models of physical processes but are also of great interest in PDE-based image processing.
2 code implementations • ICCV 2021 • Junjie Ke, Qifei Wang, Yilin Wang, Peyman Milanfar, Feng Yang
To accommodate this, the input images are usually resized and cropped to a fixed shape, causing image quality degradation.
no code implementations • CVPR 2021 • Yilin Wang, Junjie Ke, Hossein Talebi, Joong Gon Yim, Neil Birkbeck, Balu Adsumilli, Peyman Milanfar, Feng Yang
Besides the subjective ratings and content labels of the dataset, we also propose a DNN-based framework to thoroughly analyze importance of content, technical quality, and compression level in perceptual quality.
2 code implementations • ICCV 2021 • Yinxiao Li, Pengchong Jin, Feng Yang, Ce Liu, Ming-Hsuan Yang, Peyman Milanfar
Most video super-resolution methods focus on restoring high-resolution video frames from low-resolution videos without taking into account compression.
no code implementations • CVPR 2022 • Innfarn Yoo, Huiwen Chang, Xiyang Luo, Ondrej Stava, Ce Liu, Peyman Milanfar, Feng Yang
Digital watermarking is widely used for copyright protection.
1 code implementation • ICLR 2022 • Mangal Prakash, Mauricio Delbracio, Peyman Milanfar, Florian Jug
This work presents an interpretable approach for unsupervised and diverse image restoration.
1 code implementation • ICCV 2021 • Gregory Vaksman, Michael Elad, Peyman Milanfar
Our algorithm augments video sequences with patch-craft frames and feeds them to a CNN.
Ranked #2 on
Video Denoising
on DAVIS sigma20
4 code implementations • ICCV 2021 • Hossein Talebi, Peyman Milanfar
Moreover, we show that the proposed resizer can also be useful for fine-tuning the classification baselines for other vision tasks.
1 code implementation • 6 Mar 2021 • Guy Ohayon, Theo Adrai, Gregory Vaksman, Michael Elad, Peyman Milanfar
We showcase our proposed method with a novel denoiser architecture that achieves the reformed denoising goal and produces vivid and diverse outcomes in immoderate noise levels.
no code implementations • 1 Mar 2021 • Juan Carlos Mier, Eddie Huang, Hossein Talebi, Feng Yang, Peyman Milanfar
In this paper we propose the largest image compression quality dataset to date with human perceptual preferences, enabling the use of deep learning, and we develop a full reference perceptual quality assessment metric for lossy image compression that outperforms the existing state-of-the-art methods.
no code implementations • 17 Feb 2021 • Mauricio Delbracio, Damien Kelly, Michael S. Brown, Peyman Milanfar
The first mobile camera phone was sold only 20 years ago, when taking pictures with one's phone was an oddity, and sharing pictures online was unheard of.
2 code implementations • 16 Dec 2020 • Mauricio Delbracio, Hossein Talebi, Peyman Milanfar
More explicitly, we show that in imaging applications such as denoising, super-resolution, demosaicing, deblurring and JPEG artifact removal, the proposed learning loss outperforms the current state-of-the-art on reference-based perceptual losses.
no code implementations • 16 Dec 2020 • Mauricio Delbracio, Ignacio Garcia-Dorado, Sungjoon Choi, Damien Kelly, Peyman Milanfar
The proposed method estimates and removes mild blur from a 12MP image on a modern mobile phone in a fraction of a second.
1 code implementation • ICCV 2021 • Abdullah Abuolaim, Mauricio Delbracio, Damien Kelly, Michael S. Brown, Peyman Milanfar
Leveraging these realistic synthetic DP images, we introduce a recurrent convolutional network (RCN) architecture that improves deblurring results and is suitable for use with single-frame and multi-frame data (e. g., video) captured by DP sensors.
no code implementations • 21 Nov 2020 • Hossein Talebi, Ehsan Amid, Peyman Milanfar, Manfred K. Warmuth
Training a model on these pairwise preferences is a common deep learning approach.
no code implementations • 10 Oct 2020 • Qifei Wang, Junjie Ke, Joshua Greaves, Grace Chu, Gabriel Bender, Luciano Sbaiz, Alec Go, Andrew Howard, Feng Yang, Ming-Hsuan Yang, Jeff Gilbert, Peyman Milanfar
This approach effectively reduces the total number of parameters and FLOPS, encouraging positive knowledge transfer while mitigating negative interference across domains.
no code implementations • 3 Aug 2020 • Xiyang Luo, Hossein Talebi, Feng Yang, Michael Elad, Peyman Milanfar
As a case study, we focus on the design of the quantization tables in the JPEG compression standard.
no code implementations • 1 Aug 2020 • Regev Cohen, Michael Elad, Peyman Milanfar
Two such methods are the Plug-and-Play Prior (PnP) and Regularization by Denoising (RED).
no code implementations • CVPR 2020 • Innfarn Yoo, Xiyang Luo, Yilin Wang, Feng Yang, Peyman Milanfar
DitherNet manipulates the input image to reduce color banding artifacts and provides an alternative to traditional dithering.
1 code implementation • 4 Mar 2020 • David Berthelot, Peyman Milanfar, Ian Goodfellow
That is to say, instead of generating an arbitrary image as a sample from the manifold of natural images, we propose to sample images from a particular "subspace" of natural images, directed by a low-resolution image from the same subspace.
no code implementations • 26 Feb 2020 • Xiang Zhu, Hossein Talebi, Xinwei Shi, Feng Yang, Peyman Milanfar
We propose a realistic training data generation model for commercial satellite imagery products, which includes not only the imaging process on satellites but also the post-process on the ground.
satellite image super-resolution
Single Image Super Resolution
no code implementations • 22 Feb 2020 • Ignacio Garcia-Dorado, Pascal Getreuer, Bartlomiej Wronski, Peyman Milanfar
We present a framework for interactive design of new image stylizations using a wide range of predefined filter blocks.
no code implementations • 1 Feb 2020 • Hossein Talebi, Damien Kelly, Xiyang Luo, Ignacio Garcia Dorado, Feng Yang, Peyman Milanfar, Michael Elad
In this work we aim to break the unholy connection between bit-rate and image quality, and propose a way to circumvent compression artifacts by pre-editing the incoming image and modifying its content to fit the given bits.
no code implementations • CVPR 2020 • Xiyang Luo, Ruohan Zhan, Huiwen Chang, Feng Yang, Peyman Milanfar
Watermarking is the process of embedding information into an image that can survive under distortions, while requiring the encoded image to have little or no perceptual difference from the original image.
1 code implementation • 17 Nov 2019 • Gregory Vaksman, Michael Elad, Peyman Milanfar
This work proposes a novel lightweight learnable architecture for image denoising, and presents a combination of supervised and unsupervised training of it, the first aiming for a universal denoiser and the second for adapting it to the incoming image.
no code implementations • 28 Sep 2019 • Meyer Scetbon, Michael Elad, Peyman Milanfar
The question we address in this paper is whether K-SVD was brought to its peak in its original conception, or whether it can be made competitive again.
3 code implementations • 8 May 2019 • Bartlomiej Wronski, Ignacio Garcia-Dorado, Manfred Ernst, Damien Kelly, Michael Krainin, Chia-Kai Liang, Marc Levoy, Peyman Milanfar
In this paper, we supplant the use of traditional demosaicing in single-frame and burst photography pipelines with a multiframe super-resolution algorithm that creates a complete RGB image directly from a burst of CFA raw images.
1 code implementation • 25 Mar 2019 • Gary Mataev, Michael Elad, Peyman Milanfar
Inverse problems in imaging are extensively studied, with a variety of strategies, tools, and theory that have been accumulated over the years.
Ranked #7 on
Image Super-Resolution
on Set14 - 8x upscaling
no code implementations • 23 Apr 2018 • Peyman Milanfar
The premise of our work is deceptively familiar: A black box $f(\cdot)$ has altered an image $\mathbf{x} \rightarrow f(\mathbf{x})$.
no code implementations • 9 Mar 2018 • Frank Ong, Peyman Milanfar, Pascal Getreuer
In this work, we broadly connect kernel-based filtering (e. g. approaches such as the bilateral filters and nonlocal means, but also many more) with general variational formulations of Bayesian regularized least squares, and the related concept of proximal operators.
no code implementations • 16 Feb 2018 • Sungjoon Choi, John Isidoro, Pascal Getreuer, Peyman Milanfar
Denoising is a fundamental imaging problem.
no code implementations • 18 Dec 2017 • Ignacio Garcia-Dorado, Pascal Getreuer, Madison Le, Robin Debreuil, Alex Kauffmann, Peyman Milanfar
In parallel to this manual design, we propose a novel procedural approach that automatically assembles sequences of filters for innovative results.
Graphics
no code implementations • 7 Dec 2017 • Hossein Talebi, Peyman Milanfar
Learning a typical image enhancement pipeline involves minimization of a loss function between enhanced and reference images.
no code implementations • 29 Nov 2017 • Pascal Getreuer, Ignacio Garcia-Dorado, John Isidoro, Sungjoon Choi, Frank Ong, Peyman Milanfar
The Rapid and Accurate Image Super Resolution (RAISR) method of Romano, Isidoro, and Milanfar is a computationally efficient image upscaling method using a trained set of filters.
4 code implementations • 15 Sep 2017 • Hossein Talebi, Peyman Milanfar
Automatically learned quality assessment for images has recently become a hot topic due to its usefulness in a wide variety of applications such as evaluating image capture pipelines, storage techniques and sharing media.
Ranked #4 on
Aesthetics Quality Assessment
on AVA
2 code implementations • 9 Nov 2016 • Yaniv Romano, Michael Elad, Peyman Milanfar
As opposed to the $P^3$ method, we offer Regularization by Denoising (RED): using the denoising engine in defining the regularization of the inverse problem.
1 code implementation • 26 Sep 2016 • Sujoy Kumar Biswas, Peyman Milanfar
Pedestrian detection in thermal infrared images poses unique challenges because of the low resolution and noisy nature of the image.
2 code implementations • 10 Sep 2016 • Michael Elad, Peyman Milanfar
Recent work on this problem adopting Convolutional Neural-networks (CNN) ignited a renewed interest in this field, due to the very impressive results obtained.
no code implementations • 23 Jun 2016 • Hossein Talebi, Peyman Milanfar
A novel, fast and practical way of enhancing images is introduced in this paper.
no code implementations • 3 Jun 2016 • Yaniv Romano, John Isidoro, Peyman Milanfar
Our approach additionally includes an extremely efficient way to produce an image that is significantly sharper than the input blurry one, without introducing artifacts such as halos and noise amplification.