Peyman Milanfar

Fast and robust multiframe super resolution

Super-resolution reconstruction produces one or a set of high-resolution images from a set of low-resolution images. In the last two decades, a variety of super-resolution methods have been proposed. These methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses their shortcomings. We propose an alternate approach using L/sub 1/ norm minimization and robust regularization based on a bilateral prior to deal with different data and noise models. This computationally inexpensive method is robust to errors in motion and blur estimation and results in images with sharp edges. Simulation results confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods.

RAISR: Rapid and Accurate Image Super Resolution

Given an image, we wish to produce an image of larger size with significantly more pixels and higher image quality. This is generally known as the single image super-resolution problem. The idea is that with sufficient training data (corresponding pairs of low and high resolution images) we can learn set of filters (i.e., a mapping) that when applied to given image that is not in the training set, will produce a higher resolution version of it, where the learning is preferably low complexity. In our proposed approach, the run-time is more than one to two orders of magnitude faster than the best competing methods currently available, while producing results comparable or better than state-of-the-art. A closely related topic is image sharpening and contrast enhancement, i.e., improving the visual quality of a blurry image by amplifying the underlying details (a wide range of frequencies). 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. We illustrate how this effective sharpening algorithm, in addition to being of independent interest, can be used as a preprocessing step to induce the learning of more effective upscaling filters with built-in sharpening and contrast enhancement effect.

Style Transfer Via Texture Synthesis

Style transfer is a process of migrating a style from a given image to the content of another, synthesizing a new image, which is an artistic mixture of the two. Recent work on this problem adopting convolutional neural-networks (CNN) ignited a renewed interest in this field, due to the very impressive results obtained. There exists an alternative path toward handling the style transfer task, via the generalization of texture synthesis algorithms. This approach has been proposed over the years, but its results are typically less impressive compared with the CNN ones. In this paper, we propose a novel style transfer algorithm that extends the texture synthesis work of Kwatra et al. (2005), while aiming to get stylized images that are closer in quality to the CNN ones. We modify Kwatra’s algorithm in several key ways in order to achieve the desired transfer, with emphasis on a consistent way for keeping the content intact in selected regions, while producing hallucinated and rich style in others. The results obtained are visually pleasing and diverse, shown to be competitive with the recent CNN style transfer algorithms. The proposed algorithm is fast and flexible, being able to process any pair of content + style images.

Visual saliency in noisy images.

The human visual system possesses the remarkable ability to pick out salient objects in images. Even more impressive is its ability to do the very same in the presence of disturbances. In particular, the ability persists despite the presence of noise, poor weather, and other impediments to perfect vision. Meanwhile, noise can significantly degrade the accuracy of automated computational saliency detection algorithms. In this article, we set out to remedy this shortcoming. Existing computational saliency models generally assume that the given image is clean, and a fundamental and explicit treatment of saliency in noisy images is missing from the literature. Here we propose a novel and statistically sound method for estimating saliency based on a nonparametric regression framework and investigate the stability of saliency models for noisy images and analyze how state-of-the-art computational models respond to noisy visual stimuli. The proposed model of saliency at a pixel of interest is a data-dependent weighted average of dissimilarities between a center patch around that pixel and other patches. To further enhance the degree of accuracy in predicting the human fixations and of stability to noise, we incorporate a global and multiscale approach by extending the local analysis window to the entire input image, even further to multiple scaled copies of the image. Our method consistently outperforms six other state-of-the-art models (Bruce & Tsotsos, 2009; Garcia-Diaz, Fdez-Vidal, Pardo, & Dosil, 2012; Goferman, Zelnik-Manor, & Tal, 2010; Hou & Zhang, 2007; Seo & Milanfar, 2009; Zhang, Tong, & Marks, 2008) for both noise-free and noisy cases.

Linear Support Tensor Machine With LSK Channels: Pedestrian Detection in Thermal Infrared Images

Pedestrian detection in thermal infrared images poses unique challenges because of the low resolution and noisy nature of the image. Here, we propose a mid-level attribute in the form of the multidimensional template, or tensor, using local steering kernel (LSK) as low-level descriptors for detecting pedestrians in far infrared images. LSK is specifically designed to deal with intrinsic image noise and pixel level uncertainty by capturing local image geometry succinctly instead of collecting local orientation statistics (e.g., histograms in histogram of oriented gradients). In order to learn the LSK tensor, we introduce a new image similarity kernel following the popular maximum margin framework of support vector machines facilitating a relatively short and simple training phase for building a rigid pedestrian detector. Tensor representation has several advantages, and indeed, LSK templates allow exact acceleration of the sluggish but de facto sliding window-based detection methodology with multichannel discrete Fourier transform, facilitating very fast and efficient pedestrian localization. The experimental studies on publicly available thermal infrared images justify our proposals and model assumptions. In addition, the proposed work also involves the release of our in-house annotations of pedestrians in more than 17 000 frames of OSU color thermal database for the purpose of sharing with the research community.

Fast Multilayer Laplacian Enhancement

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 filters 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); and 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.

Turbo denoising for mobile photographic applications

We propose a new denoising algorithm for camera pipelines and other photographic applications. We aim for a scheme that is (1) fast enough to be practical even for mobile devices, and (2) handles the realistic content dependent noise in real camera captures. Our scheme consists of a simple two-stage non-linear processing. We introduce a new form of boosting/blending which proves to be very effective in restoring the details lost in the first denoising stage. We also employ IIR filtering to significantly reduce the computation time. Further, we incorporate a novel noise model to address the content dependent noise. For realistic camera noise, our results are competitive with BM3D, but with nearly 400 times speedup.

A pull-push method for fast non-local means filtering

Non-local means filtering (NLM), has garnered a large amount of interest in the image processing community due to its capability to exploit image patch self-similarity in order to effectively filter noisy images. However, the computational complexity of non-local means filtering is the product of three different factors; namely, O(NDK), where K is the number of filter kernel taps (e.g. search window size), D is the number of patch taps, and N is number of pixels. We propose a fast approximation of non-local means filtering using the multiscale methodology of the pull-push scattered data interpolation method. By using NLM with a small filter kernel to selectively propagate filtering results and noise variance estimates from fine to coarse scales and back, the process can be used to provide comparable filtering capability to brute force NLM but with algorithmic complexity that is linear in the number of image pixels and the patch comparison taps, O(ND). In practical application, we demonstrate its denoising capability is comparable to NLM with much larger filter kernels, but at a fraction of the computational cost.

A new class of image filters without normalization

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 filter coefficients so that they sum to one. This concept is generally taken for granted, but is particularly important where nonlinear 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 the un-normalized affinity weights can be effectively used in applications such as image smoothing, sharpening and detail enhancement.

Pull-Push Non-local Means with Guided and Burst Filtering Capabilities

Non-local means filtering (NLM) has cultivated a large amount of work in the computational imaging community due to its ability to use the self-similarity of image patches in order to more accurately filter noisy images. However, non-local means filtering has a computational complexity that is the product of three different factors, namely, (O(NPK)), where K is the number of filter kernel taps (e.g., search window size), (P) is the number of taps in the patches used for comparison, and (N) is number of pixels in the image. We propose a fast approximation of non-local means filtering using the multiscale methodology of the pull-push scattered data interpolation method. By using NLM with a small filter kernel to selectively propagate filtering results and noise variance estimates from fine to coarse scales and back, the process can be used to provide comparable filtering capability to brute force NLM but with algorithmic complexity that is decoupled from the kernel size, K. We demonstrate that its denoising capability is comparable to NLM with much larger filter kernels, but at a fraction of the computational cost. In addition to this, we demonstrate extensions to the approach that allows for guided filtering using a reference image as well as motion compensated multi-image burst denoising. The motion compensation technique is notably efficient and effective in this context since it reuses the multiscale patch comparison computations required by the pull-push NLM algorithm.