Computational Cameras: Approaches, Benefits and Limits

A computational camera uses a combination of optics and software to produce images that cannot be taken with traditional cameras. In the last decade, computational imaging has emerged as a vibrant field of research. A wide variety of computational cameras have been demonstrated - some designed to achieve new imaging functionalities and others to reduce the complexity of traditional imaging. In this article, we describe how computational cameras have evolved and present a taxonomy for the technical approaches they use. We explore the benefits and limits of computational imaging, and describe how it is related to the adjacent and overlapping fields of digital imaging, computational photography and computational image sensors

Non-parametric Methods for Automatic Exposure Control, Radiometric Calibration and Dynamic Range Compression

Imaging systems are essential to a wide range of modern day applications. With the continuous advancement in imaging systems, there is an on-going need to adapt and improve the imaging pipeline running inside the imaging systems. In this thesis, methods are presented to improve the imaging pipeline of digital cameras. Here we present three methods to improve important phases of the imaging process, which are (i) ``Automatic exposure adjustment'' (ii) ``Radiometric calibration'' (iii) ''High dynamic range compression''. These contributions touch the initial, intermediate and final stages of imaging pipeline of digital cameras. For exposure control, we propose two methods. The first makes use of CCD-based equations to formulate the exposure control problem. To estimate the exposure time, an initial image was acquired for each wavelength channel to which contrast adjustment techniques were applied. This helps to recover a reference cumulative distribution function of image brightness at each channel. The second method proposed for automatic exposure control is an iterative method applicable for a broad range of imaging systems. It uses spectral sensitivity functions such as the photopic response functions for the generation of a spectral power image of the captured scene. A target image is then generated using the spectral power image by applying histogram equalization. The exposure time is hence calculated iteratively by minimizing the squared difference between target and the current spectral power image. Here we further analyze the method by performing its stability and controllability analysis using a state space representation used in control theory. The applicability of the proposed method for exposure time calculation was shown on real world scenes using cameras with varying architectures. Radiometric calibration is the estimate of the non-linear mapping of the input radiance map to the output brightness values. The radiometric mapping is represented by the camera response function with which the radiance map of the scene is estimated. Our radiometric calibration method employs an L1 cost function by taking advantage of Weisfeld optimization scheme. The proposed calibration works with multiple input images of the scene with varying exposure. It can also perform calibration using a single input with few constraints. The proposed method outperforms, quantitatively and qualitatively, various alternative methods found in the literature of radiometric calibration. Finally, to realistically represent the estimated radiance maps on low dynamic range display (LDR) devices, we propose a method for dynamic range compression. Radiance maps generally have higher dynamic range (HDR) as compared to the widely used display devices. Thus, for display purposes, dynamic range compression is required on HDR images. Our proposed method generates few LDR images from the HDR radiance map by clipping its values at different exposures. Using contrast information of each LDR image generated, the method uses an energy minimization approach to estimate the probability map of each LDR image. These probability maps are then used as label set to form final compressed dynamic range image for the display device. The results of our method were compared qualitatively and quantitatively with those produced by widely cited and professionally used methods

Visual image processing in various representation spaces for documentary preservation

This thesis establishes an advanced image processing framework for the enhancement and restoration of historical document images (HDI) in both intensity (gray-scale or color) and multispectral (MS) representation spaces. It provides three major contributions: 1) the binarization of gray-scale HDI; 2) the visual quality restoration of MS HDI; and 3) automatic reference data (RD) estimation for HDI binarization. HDI binarization is one of the enhancement techniques that produces bi-level information which is easy to handle using methods of analysis (OCR, for instance) and is less computationally costly to process than 256 levels of grey or color images. Restoring the visual quality of HDI in an MS representation space enhances their legibility, which is not possible with conventional intensity-based restoration methods, and HDI legibility is the main concern of historians and librarians wishing to transfer knowledge and revive ancient cultural heritage. The use of MS imaging systems is a new and attractive research trend in the field of numerical processing of cultural heritage documents. In this thesis, these systems are also used for automatically estimating more accurate RD to be used for the evaluation of HDI binarization algorithms in order to track the level of human performance. Our first contribution, which is a new adaptive method of intensity-based binarization, is defined at the outset. Since degradation is present over document images, binarization methods must be adapted to handle degradation phenomena locally. Unfortunately, these methods are not effective, as they are not able to capture weak text strokes, which results in a deterioration of the performance of character recognition engines. The proposed approach first detects a subset of the most probable text pixels, which are used to locally estimate the parameters of the two classes of pixels (text and background), and then performs a simple maximum likelihood (ML) to locally classify the remaining pixels based on their class membership. To the best of our knowledge, this is the first time local parameter estimation and classification in an ML framework has been introduced for HDI binarization with promising results. A limitation of this method in the case with as the intensity-based methods of enhancement is that they are not effective in dealing with severely degraded HDI. Developing more advanced methods based on MS information would be a promising alternative avenue of research. In the second contribution, a novel approach to the visual restoration of HDI is defined. The approach is aimed at providing end users (historians, librarians, etc..) with better HDI visualization, specifically; it aims to restore them from degradations, while keeping the original appearance of the HDI intact. Practically, this problem cannot be solved by conventional intensity-based restoration methods. To cope with these limitations, MS imaging is used to produce additional spectral images in the invisible light (infrared and ultraviolet) range, which gives greater contrast to objects in the documents. The inpainting-based variational framework proposed here for HDI restoration involves isolating the degradation phenomena in the infrared spectral images, and then inpainting them in the visible spectral images. The final color image to visualize is therefore reconstructed from the restored visible spectral images. To the best of our knowledge, this is the first time the inpainting technique has been introduced for MS HDI. The experimental results are promising, and our objective, in collaboration with the BAnQ (Bibliothèque et Archives nationales de Québec), is to push heritage documents into the public domain and build an intelligent engine for accessing them. It is useful to note that the proposed model can be extended to other MS-based image processing tasks. Our third contribution is presented, which is to consider a new problem of RD (reference data) estimation, in order to show the importance of working with MS images rather than gray-scale or color images. RDs are mandatory for comparing different binarization algorithms, and they are usually generated by an expert. However, an expert’s RD is always subject to mislabeling and judgment errors, especially in the case of degraded data in restricted representation spaces (gray-scale or color images). In the proposed method, multiple RD generated by several experts are used in combination with MS HDI to estimate new, more accurate RD. The idea is to include the agreement of experts about labels and the multivariate data fidelity in a single Bayesian classification framework to estimate the a posteriori probability of new labels forming the final estimated RD. Our experiments show that estimated RD are more accurate than an expert’s RD. To the best of our knowledge, no similar work to combine binary data and multivariate data for the estimation of RD has been conducted