Many automated processes such as auto-piloting rely on a good semantic segmentation as a critical component. To speed up performance, it is common to downsample the input frame. However, this comes at the cost of missed small objects and reduced accuracy at semantic boundaries. To address this problem, we propose a new content-adaptive downsampling technique that learns to favor sampling locations near semantic boundaries of target classes. Cost-performance analysis shows that our method consistently outperforms the uniform sampling improving balance between accuracy and computational efficiency. Our adaptive sampling gives segmentation with better quality of boundaries and more reliable support for smaller-size objects.

We presentPiCam(Pelican Imaging Camera-Array), an ultra-thin high performance monolithic camera array, that captures light fields and synthesizes high resolution images along with a range image (scene depth) through integrated parallax detection and superresolution. The camera is passive, supporting both stills and video, low light capable, and small enough to be included in the next generation of mobile devices including smartphones. Prior works [Rander et al. 1997; Yang et al. 2002; Zhang and Chen 2004; Tanida et al. 2001; Tanida et al. 2003; Duparré et al. 2004] in camera arrays have explored multiple facets of light field capture - from viewpoint synthesis, synthetic refocus, computing range images, high speed video, and micro-optical aspects of system miniaturization. However, none of these have addressed the modifications needed to achieve the strict form factor and image quality required to make array cameras practical for mobile devices. In our approach, we customize many aspects of the camera array including lenses, pixels, sensors, and software algorithms to achieve imaging performance and form factor comparable to existing mobile phone cameras. Our contributions to the post-processing of images from camera arrays include a cost function for parallax detection that integrates across multiple color channels, and a regularized image restoration (superresolution) process that takes into account all the system degradations and adapts to a range of practical imaging conditions. The registration uncertainty from the parallax detection process is integrated into a Maximum-a-Posteriori formulation that synthesizes an estimate of the high resolution image and scene depth. We conclude with some examples of our array capabilities such as postcapture (still) refocus, video refocus, view synthesis to demonstrate motion parallax, 3D range images, and briefly address future work.

In this paper, we propose a denoising method motivated by our previous analysis of the performance bounds for image denoising. Insights from that study are used here to derive a high-performance practical denoising algorithm. We propose a patch-based Wiener filter that exploits patch redundancy for image denoising. Our framework uses both geometrically and photometrically similar patches to estimate the different filter parameters. We describe how these parameters can be accurately estimated directly from the input noisy image. Our denoising approach, designed for near-optimal performance (in the mean-squared error sense), has a sound statistical foundation that is analyzed in detail. The performance of our approach is experimentally verified on a variety of images and noise levels. The results presented here demonstrate that our proposed method is on par or exceeding the current state of the art, both visually and quantitatively.

We address the effects of noise in low-light images in this paper. Color images are usually captured by a sensor with a color filter array (CFA). This requires a demosaicing process to generate a full color image. The captured images typically have low signal-to-noise ratio, and the demosaicing step further corrupts the image, which we show to be the leading cause of visually objectionable random noise patterns (splotches). To avoid this problem, we propose a combined framework of denoising and demosaicing, where we use information about the image inferred in the denoising step to perform demosaicing. Our experiments show that such a framework results in sharper low-light images that are devoid of splotches and other noise artifacts.

In our previous work [1], we formulated the fundamental limits of image denoising. In this paper, we propose a practical algorithm where the motivation is to realize a locally optimal denoising filter that achieves the lower bound. The proposed method is a patch-based Wiener filter that takes advantage of both geometrically and photometrically similar patches. The resultant approach has a nice statistical foundation while producing denoising results that are comparable to or exceeding the current state-of-the-art, both visually and quantitatively.

Recently, in a previous work, we proposed a way to bound how well any given image can be denoised. The bound was computed directly from the noise-free image that was assumed to be available. In this work, we extend the formulation to the more practical case where no ground truth is available. We show that the parameters of the bounds, namely the cluster covariances and level of redundancy for patches in the image, can be estimated directly from the noise corrupted image. Further, we analyze the bounds formulation to show that these two parameters are interdependent and they, along with the bounds formulation as a whole, have a nice information-theoretic interpretation as well. The results are verified through a variety of well-motivated experiments.

In this paper, we study the fundamental performance limits of image denoising where the aim is to recover the original image from its noisy observation. Our study is based on a general class of estimators whose bias can be modeled to be affine. A bound on the performance in terms of mean squared error (MSE) of the recovered image is derived in a Bayesian framework. In this work, we assume that the original image is available, from which we learn the image statistics. Performances of some current state-of-the-art methods are compared to our MSE bounds for some commonly used experimental images. These show that some gain in denoising performance is yet to be achieved.

In [1], we derived an expression for the fundamental limit to image denoising assuming that the noise-free image is available. In this paper, we propose an estimator for the bound on the mean squared error given only the noisy image and noise characteristics. To do this, we make use of an assortment of independently collected noise-free images from which prior information about the noisy image is learned. We show that even for reasonably low input signal-to-noise levels, our method can predict the denoising bound with accuracy.

Image denoising has been a well studied problem in the field of image processing. Yet researchers continue to focus attention on it to better the current state-of-the-art. Recently proposed methods take different approaches to the problem and yet their denoising performances are comparable. A pertinent question then to ask is whether there is a theoretical limit to denoising performance and, more importantly, are we there yet? As camera manufacturers continue to pack increasing numbers of pixels per unit area, an increase in noise sensitivity manifests itself in the form of a noisier image. We study the performance bounds for the image denoising problem. Our work in this paper estimates a lower bound on the mean squared error of the denoised result and compares the performance of current state-of-the-art denoising methods with this bound. We show that despite the phenomenal recent progress in the quality of denoising algorithms, some room for improvement still remains for a wide class of general images, and at certain signal-to-noise levels. Therefore, image denoising is not dead--yet.

In this paper, we study the Papoulis–Gerchberg (PG) method and its applications to domains of image restoration such as super-resolution (SR) and inpainting. We show that the method performs well under certain conditions. We then suggest improvements to the method to achieve better SR and inpainting results. The modification applied to the SR process also allows us to apply the method to a larger class of images by doing away with some of the restrictions inherent in the classical PG method. We also present results to demonstrate the performance of the proposed techniques.

In this paper, we propose K-LLD: a patch-based, locally adaptive denoising method based on clustering the given noisy image into regions of similar geometric structure. In order to effectively perform such clustering, we employ as features the local weight functions derived from our earlier work on steering kernel regression . These weights are exceedingly informative and robust in conveying reliable local structural information about the image even in the presence of significant amounts of noise. Next, we model each region (or cluster)-which may not be spatially contiguous-by "learning" a best basis describing the patches within that cluster using principal components analysis. This learned basis (or "dictionary") is then employed to optimally estimate the underlying pixel values using a kernel regression framework. An iterated version of the proposed algorithm is also presented which leads to further performance enhancements. We also introduce a novel mechanism for optimally choosing the local patch size for each cluster using Stein's unbiased risk estimator (SURE). We illustrate the overall algorithm's capabilities with several examples. These indicate that the proposed method appears to be competitive with some of the most recently published state of the art denoising methods.