Hossein Talebi
I am a Senior Staff Software Engineer at Google Research. Our team works on the intersection of computational photography and machine learning. My main focus is on perceptual quality assessment, deep image enhancement, and image compression. Prior to Google, I attended the University of California, Santa Cruz to obtain my Ph.D in Electrical Engineering.
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Soft Diffusion: Score Matching with General Corruptions
Giannis Daras
Alexandros Dimakis
Transactions on Machine Learning Research (TMLR) (2023)
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We define a broader family of corruption processes that generalizes previously known diffusion models. To reverse these general diffusions, we propose a new objective called Soft Score Matching. Soft Score Matching incorporates the degradation process in the network and provably learns the score function for any linear corruption process. Our new loss trains the model to predict a clean image, that after corruption, matches the diffused observation. This objective learns the gradient of the likelihood under suitable regularity conditions for the family of linear corruption processes. We further develop an algorithm to select the corruption levels for general diffusion processes and a novel sampling method that we call Momentum Sampler. We show experimentally that our framework works for general linear corruption processes, such as Gaussian blur and masking. Our method outperforms all linear diffusion models on CelebA-64 achieving FID score 1.85. We also show computational benefits compared to vanilla denoising diffusion.
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MAXIM: Multi-Axis MLP for Image Processing
Han Zhang
Alan Bovik
IEEE/CVF Conference on Computer Vision and Pattern Recognition (2022)
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Recent progress on Transformers and MLP-like models has shown new architecture design paradigms on many computer vision tasks. However, efficacy and efficiency of these models for low-level vision tasks have not been studied extensively. In this paper, we present MAXIM, a general image processing architecture with multi-axis gated MLPs, to advance the possibility of global operators for low-level vision. Our single-stage MAXIM backbone shares a UNet-shaped hierarchy structure and enjoys a long-range interaction brought by spatial-gated MLPs. Specifically, MAXIM contains two MLP-based building blocks. First, we devise a multi-axis gated MLP that allows efficient and scalable spatial mixing of local and global information. Second, we propose a cross-gating block, an alternative to cross-attention, which accounts for cross-example mutual conditioning. Both modules are exclusively based on MLPs, but benefit from being both global and `fully-convolutional,' two desired properties for low-level vision tasks. Our extensive experimental results show that our proposed MAXIM model achieves state-of-the-art performance on more than ten benchmarks across a range of image processing tasks including denoising, deblurring, deraining, dehazing, and enhancement with less or comparable parameters and FLOPs.
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Deblurring via Stochastic Refinement
Jay Whang
Chitwan Saharia
Alexandros Dimakis
CVPR (2022)
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Image deblurring is an ill-posed problem with multiple plausible solutions given a single input image. However, most existing methods produce a deterministic estimate of the clean image and are trained to minimize pixel-level distortion. These metrics are known to be poorly correlated with human perception, and often lead to unrealistic reconstructions.
We present an alternative framework for single-image blind deblurring based on conditional diffusion models. 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 single input. This leads to a significant improvement in perceptual quality over existing state-of-the-art methods across multiple standard benchmarks. Our predict-and-refine approach also enables much more efficient sampling compared to the standard diffusion model. Combined with a carefully tuned network architecture and inference procedure, our method is shown to be competitive in terms of traditional quantitative distortion metrics such as PSNR. These results show clear benefits of stochastic diffusion-based methods for deblurring and challenge the widely used strategy of producing a single, deterministic reconstruction.
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MaxViT: Multi-Axis Vision Transformer
Han Zhang
Alan Bovik
European Conference on Computer Vision (ECCV) (2022)
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Transformers have recently gained significant attention in the computer vision community. However, the lack of scalability of self-attention mechanisms with respect to image size has limited their wide adoption in state-of-the-art vision backbones. In this paper we introduce an efficient and scalable attention model we call multi-axis attention, which consists of two aspects: blocked local and dilated global attention. These design choices allow global-local spatial interactions on arbitrary input resolutions with only linear complexity. We also present a new architectural element by effectively blending our proposed attention model with convolutions, and accordingly propose a simple hierarchical vision backbone, dubbed MaxViT, by simply repeating the basic building block over multiple stages. Notably, MaxViT is able to “see” globally throughout the entire network, even in earlier, high-resolution stages. We demonstrate the effectiveness of our model on a broad spectrum of vision tasks. On image classification, MaxViT achieves state-of-the-art performance under various settings: without extra data, MaxViT attains 86.5% ImageNet-1K top-1 accuracy; with ImageNet-21K pre-training,
our model achieves 88.7% top-1 accuracy. For downstream tasks, MaxViT as a backbone delivers favorable performance on object detection as well as visual aesthetic assessment. 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. The source code and trained models will be available at https://github.com/google-research/maxvit.
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Deep Perceptual Image Quality Assessment for Compression
ICIP 2021 International Conference on Image Processing (2021)
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Lossy Image compression is necessary for efficient storage and transfer of data. Typically the trade-off between bit-rate and quality determines the optimal compression level. This makes the image quality metric an integral part of any imaging system. While the existing full-reference metrics such as PSNR and SSIM may be less sensitive to perceptual quality, the recently introduced learning methods may fail to generalize to unseen data. 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. We show that the proposed model can effectively learn from thousands of examples available in the new dataset, and consequently it generalizes better to other unseen datasets of human perceptual preference. The CIQA dataset can be found at https://github.com/googleresearch/google-research/tree/master/CIQA
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Better Compression with Deep Pre-Editing
Michael Elad
IEEE Transactions on Image Processing (2021)
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Could we compress images via standard codecs while avoiding visible artifacts? The answer is obvious -- this is doable as long as the bit budget is generous enough. What if the allocated bit-rate for compression is insufficient? Then unfortunately, artifacts are a fact of life. Many attempts were made over the years to fight this phenomenon, with various degrees of success. 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. We design this editing operation as a learned convolutional neural network, and formulate an optimization problem for its training. Our loss takes into account a proximity between the original image and the edited one, a bit-budget penalty over the proposed image, and a no-reference image quality measure for forcing the outcome to be visually pleasing. The proposed approach is demonstrated on the popular JPEG compression, showing savings in bits and/or improvements in visual quality, obtained with intricate editing effects.
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Projected Distribution Loss for Image Enhancement
2021 IEEE International Conference on Computational Photography (ICCP), pp. 1-12
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Features obtained from object detection CNNs have been widely used for measuring perceptual similarities between images. Such differentiable metrics can be used as perceptual learning losses to train image enhancement models. However, choice of the distance function between input and target features may have a consequential impact on the performance of trained model. While using the norm of the difference between extracted features leads to limited hallucination of details, measuring distance between distributions of features may generate more textures; yet also more unrealistic details and artifacts. In this paper, we demonstrate that aggregating 1D-Wasserstein distances between CNN activations is more reliable than the existing approaches, and it can significantly improve the perceptual performance of enhancement models. 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. This means that the proposed learning loss can be plugged into different imaging frameworks and produce perceptually realistic results.
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For all the ways convolutional neural nets have revolutionized computer vision in recent years, one important aspect has received surprisingly little attention: the effect of image size on the accuracy of tasks being trained for. Typically, to be efficient, the input images are resized to a relatively small spatial resolution (e.g.224×224), and both training and inference are carried out at this resolution. The actual mechanism for this re-scaling has been an afterthought: Namely, off-the-shelf image resizers such as bilinear and bicubic are commonly used in most machine learning software frameworks. But do these resizers limit the on task performance of the trained networks? The answer is yes. Indeed, we show that the typical linear resizer can be replaced with learned resizers that can substantially improve performance. Importantly, while the classical resizers typically result in better perceptual quality of the downscaled images, our proposed learned resizers do not necessarily give better visual quality, but instead improve task performance.
Our learned image resizer is jointly trained with a baseline vision model. This learned CNN-based resizer creates machine friendly visual manipulations that lead to a consistent improvement of the end task metric over the baseline model. Specifically, here we focus on the classification task with the ImageNet dataset, and experiment with four different models to learn resizers adapted to each model. Moreover, we show that the proposed resizer can also be useful for fine-tuning the classification baselines for other vision tasks. To this end, we experiment with three different baselines to develop image quality assessment (IQA) models on the AVA dataset.
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JPEG is an old yet popular image compression format, sup-ported by all imaging devices and software packages. A key ingredientgoverning its performance are the two quantization tables (for Luma andChroma) that dictate the loss induced on each DCT coefficient. Pastwork has offered various ideas for better tuning these tables, mainly fo-cusing on rate-distortion performance and using derivative-free optimiza-tion techniques. This work offers a novel optimal tuning of these tablesvia continuous optimization, leveraging a differential implementation ofboth the JPEG encoder-decoder and an entropy estimator. This enablesus to offer a unified framework that considers the interplay between fourperformance measures: rate, distortion, perceptual quality, and classi-fication accuracy. We also propose a deep-neural network design thatautomatically assigns optimized quantization tables to each incomingimage. In all these fronts, we report a substantial boost in performanceby a simple and easily implemented modification of these tables.
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Video quality assessment for User Generated Content (UGC) is an important topic in both industry and academia. Most existing methods only focus on one aspect of the perceptual quality assessment, such as technical quality or compression artifacts. In this paper, we create a large scale dataset to comprehensively investigate characteristics of generic UGC video quality. 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. Our model is able to provide quality scores as well as human-friendly quality indicators, to bridge the gap between low level video signals to human perceptual quality. Experimental results show that our model achieves state-of-the-art correlation with Mean Opinion Scores (MOS).
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Rank-smoothed Pairwise Learning in Perceptual Quality Assessment
Ehsan Amid
(ICIP 2020) 2020 IEEE International Conference on Image Processing (2020)
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Conducting pairwise comparisons is a widely used approach in curating human perceptual preference data. Typically raters are instructed to make their choices according to a specific set of rules that address certain dimensions of image quality and aesthetics. The outcome of this process is a dataset of sampled image pairs with their associated empirical preference probabilities. Training a model on these pairwise preferences is a common deep learning approach. However, optimizing by gradient descent through mini-batch learning means that the “global” ranking of the images is not explicitly taken into account. In other words, each step of the gradient descent relies only on a limited number of pairwise comparisons. In this work, we demonstrate that regularizing the pairwise empirical probabilities with aggregated rankwise probabilities leads to a more reliable training loss. We show that training a deep image quality assessment model with our rank-smoothed loss consistently improves the accuracy of predicting human preferences.
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Super-resolving Commercial Satellite Imagery Using Realistic Training Data
Xiang Zhu
Xinwei Shi
IEEE International Conference on Image Processing 2020 (2020)
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In machine learning based single image super-resolution, the degradation model is embedded in training data generation. However, most existing satellite image super-resolution methods use a simple down-sampling model with a fixed kernel to create training images. These methods work fine on synthetic data, but do not perform well on real satellite images. We proposed 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. We also proposed a convolutional neural network optimized for satellite images. Experiments show that the proposed training data generation model is able to improve super-resolution performance on real satellite images.
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Video transcoding optimization based on input perceptual quality
SPIE Optical Engineering + Applications: Applications of Digital Image Processing XLIII (2020)
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Todays video transcoding pipelines choose transcoding parameters based on Rate-Distortion curves, which mainlyfocuses on the relative quality difference between original and transcoded videos. By investigating recentlyreleased YouTube UGC dataset, we found that people were more tolerant to the quality changes in low qualityinputs than in high quality inputs, which suggests that current transcoding framework could be further optimizedby considering input perceptual quality. An efficient machine learning based metric was proposed to detect lowquality inputs, whose bitrate can be further reduced without hurting perceptual quality. To evaluate the impacton perceptual quality, we conducted a crowd-sourcing subjective experiment, and provided a methodology toevaluate statistical significance among different treatments. The results showed that the proposed quality guidedtranscoding framework is able to reduce the average bitrate upto 5% with insignificant quality degradation.
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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 mediums. Despite the subjective nature of this problem, most existing methods only predict the mean opinion score provided by datasets such as AVA [1] and TID2013 [2]. Our approach differs from others in that we predict the distribution of human opinion scores. Our architecture also has the advantage of being significantly simpler than other methods with comparable performance. Our proposed approach relies on the success (and retraining) of proven, state-of-the-art deep object recognition networks. Our resulting network can be used to not only score images reliably and with high correlation to human perception, but also to assist with adaptation and optimization of photo editing/enhancement algorithms in a photographic pipeline. All this is done without need of a “golden” reference image, consequently allowing for single-image, semantic- and perceptually-aware, no-reference quality assessment.
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Learned Perceptual Image Enhancement
ICCP (International Conference on Computational Photography 2018) (2018)
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Learning of a typical image enhancement pipeline involves minimization of a loss function between enhanced and reference images. While L 1 and L 2 losses are perhaps the most widely used functions for this purpose, they do not necessarily lead to perceptually compelling results. In this paper, we show that adding a learned no-reference image quality metric to the loss can significantly improve enhancement operators. This metric is a CNN (convolutional neural network) trained on a large-scale dataset labelled with aesthetic preference of human raters. This loss allows us to conveniently perform back-propagation in our learning framework to simultaneously optimize for similarity to a given ground truth reference and perceptual quality. This perceptual loss is only used to train parameters of image processing operators, and does not impose any extra complexity at inference time. Our experiments demonstrate that this loss can be effective for tuning a variety of operators such as local tone mapping and dehazing.
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A novel, fast and practical way of enhancing images is introduced in this paper. Our approach builds on Laplacian operators of well-known edge-aware kernels, such as bilateral and nonlocal means, and extends these filter’s capabilities to perform more effective and fast image smoothing, sharpening and tone manipulation. We propose an approximation of the Laplacian, which does not require normalization of the kernel weights. Multiple Laplacians of the affinity weights endow our method with progressive detail decomposition of the input image from fine to coarse scale. These image components are blended by a structure mask, which avoids noise/artifact magnification or detail loss in the output image. Contributions of the proposed method to existing image editing tools are: (1) Low computational and memory requirements, making it appropriate for mobile device implementations (e.g. as a finish step in a camera pipeline), (2) A range of filtering applications from detail enhancement to denoising with only a few control parameters, enabling the user to apply a combination of various (and even opposite) filtering effects.
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A NEW CLASS OF IMAGE FILTERS WITHOUT NORMALIZATION
International Conference on Image Processing, Phoenix, Arizona (2016)
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When applying a filter to an image, it often makes practical sense to maintain the local brightness level from input to output image. This is achieved by normalizing the filer coefficients so that they sum to one. This concept is generally taken for granted, but is particularly important where non-linear filters such as the bilateral or and non-local means are concerned, where the effect on local brightness and contrast can be complex. Here we present a method for achieving the same level of control over the local filter behavior without the need for this normalization. Namely, we show how to closely approximate any normalized filter without in fact needing this normalization step. This yields a new class of filters. We derive a closed-form expression for the approximating filter and analyze its behavior, showing it to be easily controlled for quality and nearness to the exact filter, with a single parameter. Our experiments demonstrate that he un-normalized affinity weights can be effectively used in applications such as image smoothing, sharpening and detail enhancement.
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