Convolutional Deblurring for Natural Imaging

In this paper, we propose a novel design of image deblurring in the form of one-shot convolution filtering that can directly convolve with naturally blurred images for restoration. The problem of optical blurring is a common disadvantage to many imaging applications that suffer from optical imperfections. Despite numerous deconvolution methods that blindly estimate blurring in either inclusive or exclusive forms, they are practically challenging due to high computational cost and low image reconstruction quality. Both conditions of high accuracy and high speed are prerequisites for high-throughput imaging platforms in digital archiving. In such platforms, deblurring is required after image acquisition before being stored, previewed, or processed for high-level interpretation. Therefore, on-the-fly correction of such images is important to avoid possible time delays, mitigate computational expenses, and increase image perception quality. We bridge this gap by synthesizing a deconvolution kernel as a linear combination of Finite Impulse Response (FIR) even-derivative filters that can be directly convolved with blurry input images to boost the frequency fall-off of the Point Spread Function (PSF) associated with the optical blur. We employ a Gaussian low-pass filter to decouple the image denoising problem for image edge deblurring. Furthermore, we propose a blind approach to estimate the PSF statistics for two Gaussian and Laplacian models that are common in many imaging pipelines. Thorough experiments are designed to test and validate the efficiency of the proposed method using 2054 naturally blurred images across six imaging applications and seven state-of-the-art deconvolution methods.Comment: 15 pages, for publication in IEEE Transaction Image Processin

Generalized Video Deblurring for Dynamic Scenes

Several state-of-the-art video deblurring methods are based on a strong assumption that the captured scenes are static. These methods fail to deblur blurry videos in dynamic scenes. We propose a video deblurring method to deal with general blurs inherent in dynamic scenes, contrary to other methods. To handle locally varying and general blurs caused by various sources, such as camera shake, moving objects, and depth variation in a scene, we approximate pixel-wise kernel with bidirectional optical flows. Therefore, we propose a single energy model that simultaneously estimates optical flows and latent frames to solve our deblurring problem. We also provide a framework and efficient solvers to optimize the energy model. By minimizing the proposed energy function, we achieve significant improvements in removing blurs and estimating accurate optical flows in blurry frames. Extensive experimental results demonstrate the superiority of the proposed method in real and challenging videos that state-of-the-art methods fail in either deblurring or optical flow estimation.Comment: CVPR 2015 ora

Optimal prefilters for display enhancement

Creating images from a set of discrete samples is arguably the most common operation in computer graphics and image processing, lying, for example, at the heart of rendering and image downscaling techniques. Traditional tools for this task are based on classic sampling theory and are modeled under mathematical conditions which are, in most cases, unrealistic; for example, sinc reconstruction – required by Shannon theorem in order to recover a signal exactly – is impossible to achieve in practice because LCD displays perform a box-like interpolation of the samples. Moreover, when an image is made for a human to look at, it will necessarily undergo some modifications due to the human optical system and all the neural processes involved in vision. Finally, image processing practitioners noticed that sinc prefiltering – also required by Shannon theorem – often leads to visually unpleasant images. From these facts, we can deduce that we cannot guarantee, via classic sampling theory, that the signal we see in a display is the best representation of the original image we had in first place. In this work, we propose a novel family of image prefilters based on modern sampling theory, and on a simple model of how the human visual system perceives an image on a display. The use of modern sampling theory guarantees us that the perceived image, based on this model, is indeed the best representation possible, and at virtually no computational overhead. We analyze the spectral properties of these prefilters, showing that they offer the possibility of trading-off aliasing and ringing, while guaranteeing that images look sharper then those generated with both classic and state-of-the-art filters. Finally, we compare it against other solutions in a selection of applications which include Monte Carlo rendering and image downscaling, also giving directions on how to apply it in different contexts.Exibir imagens a partir de um conjunto discreto de amostras é certamente uma das operações mais comuns em computação gráfica e processamento de imagens. Ferramentas tradicionais para essa tarefa são baseadas no teorema de Shannon e são modeladas em condições matemáticas que são, na maior parte dos casos, irrealistas; por exemplo, reconstrução com sinc – necessária pelo teorema de Shannon para recuperar um sinal exatamente – é impossível na prática, já que displays LCD realizam uma reconstrução mais próxima de uma interpolação com kernel box. Além disso, profissionais em processamento de imagem perceberam que prefiltragem com sinc – também requerida pelo teorema de Shannon – em geral leva a imagens visualmente desagradáveis devido ao fenômeno de ringing: oscilações próximas a regiões de descontinuidade nas imagens. Desses fatos, deduzimos que não é possível garantir, via ferramentas tradicionais de amostragem e reconstrução, que a imagem que observamos em um display digital é a melhor representação para a imagem original. Neste trabalho, propomos uma família de prefiltros baseada em teoria de amostragem generalizada e em um modelo de como o sistema ótico do olho humano modifica uma imagem. Proposta por Unser and Aldroubi (1994), a teoria de amostragem generalizada é mais geral que o teorema proposto por Shannon, e mostra como é possível pré-filtrar e reconstruir sinais usando kernels diferentes do sinc. Modelamos o sistema ótico do olho como uma câmera com abertura finita e uma lente delgada, o que apesar de ser simples é suficiente para os nossos propósitos. Além de garantir aproximação ótima quando reconstruindo as amostras por um display e filtrando a imagem com o modelo do sistema ótico humano, a teoria de amostragem generalizada garante que essas operações são extremamente eficientes, todas lineares no número de pixels de entrada. Também, analisamos as propriedades espectrais desses filtros e de técnicas semelhantes na literatura, mostrando que é possível obter um bom tradeoff entre aliasing e ringing (principais artefatos quando lidamos com amostragem e reconstrução de imagens), enquanto garantimos que as imagens finais são mais nítidas que aquelas geradas por técnicas existentes na literatura. Finalmente, mostramos algumas aplicações da nossa técnica em melhoria de imagens, adaptação à distâncias de visualização diferentes, redução de imagens e renderização de imagens sintéticas por método de Monte Carlo

A Computational Framework for the Structural Change Analysis of 3D Volumes of Microscopic Specimens

Glaucoma, commonly observed with an elevation in the intraocular pressure level (IOP), is one of the leading causes of blindness. The lamina cribrosa is a mesh-like structure that provides axonal support for the optic nerves leaving the eye. The changes in the laminar structure under IOP elevations may result in the deaths of retinal ganglion cells, leading to vision degradation and loss. We have developed a comprehensive computational framework that can assist the study of structural changes in microscopic structures such as lamina cribrosa. The optical sectioning property of a confocal microscope facilitates imaging thick microscopic specimen at various depths without physical sectioning. The confocal microscope images are referred to as optical sections. The computational framework developed includes: 1) a multi-threaded system architecture for tracking a volume-of-interest within a microscopic specimen in a parallel computation environment using a reliable-multicast for collective-communication operations 2) a Karhunen-Loève (KL) expansion based adaptive noise prefilter for the restoration of the optical sections using an inverse restoration method 3) a morphological operator based ringing metric to quantify the ringing artifacts introduced during iterative restoration of optical sections 4) a l2 norm based error metric to evaluate the performance of optical flow algorithms without a priori knowledge of the true motion field and 5) a Compute-and-Propagate (CNP) framework for iterative optical flow algorithms. The realtime tracking architecture can convert a 2D-confocal microscope into a 4D-confocal microscope with tracking. The adaptive KL filter is suitable for realtime restoration of optical sections. The CNP framework significantly improves the speed and convergence of the iterative optical flow algorithms. Also, the CNP framework can reduce the errors in the motion field estimates due to the aperture problem. The performance of the proposed framework is demonstrated on real-life image sequences and on z-Stack datasets of random cotton fibers and lamina cribrosa of a cow retina with an experimentally induced glaucoma. The proposed framework can be used for routine laboratory and clinical investigation of microstructures such as cells and tissues, for the evaluation of complex structures such as cornea and has potential use as a surgical guidance tool

CAS-CNN: A Deep Convolutional Neural Network for Image Compression Artifact Suppression

Lossy image compression algorithms are pervasively used to reduce the size of images transmitted over the web and recorded on data storage media. However, we pay for their high compression rate with visual artifacts degrading the user experience. Deep convolutional neural networks have become a widespread tool to address high-level computer vision tasks very successfully. Recently, they have found their way into the areas of low-level computer vision and image processing to solve regression problems mostly with relatively shallow networks. We present a novel 12-layer deep convolutional network for image compression artifact suppression with hierarchical skip connections and a multi-scale loss function. We achieve a boost of up to 1.79 dB in PSNR over ordinary JPEG and an improvement of up to 0.36 dB over the best previous ConvNet result. We show that a network trained for a specific quality factor (QF) is resilient to the QF used to compress the input image - a single network trained for QF 60 provides a PSNR gain of more than 1.5 dB over the wide QF range from 40 to 76.Comment: 8 page