Accurate depth from defocus estimation with video-rate implementation

The science of measuring depth from images at video rate using „defocus‟ has been investigated. The method required two differently focussed images acquired from a single view point using a single camera. The relative blur between the images was used to determine the in-focus axial points of each pixel and hence depth. The depth estimation algorithm researched by Watanabe and Nayar was employed to recover the depth estimates, but the broadband filters, referred as the Rational filters were designed using a new procedure: the Two Step Polynomial Approach. The filters designed by the new model were largely insensitive to object texture and were shown to model the blur more precisely than the previous method. Experiments with real planar images demonstrated a maximum RMS depth error of 1.18% for the proposed filters, compared to 1.54% for the previous design. The researched software program required five 2D convolutions to be processed in parallel and these convolutions were effectively implemented on a FPGA using a two channel, five stage pipelined architecture, however the precision of the filter coefficients and the variables had to be limited within the processor. The number of multipliers required for each convolution was reduced from 49 to 10 (79.5% reduction) using a Triangular design procedure. Experimental results suggested that the pipelined processor provided depth estimates comparable in accuracy to the full precision Matlab‟s output, and generated depth maps of size 400 x 400 pixels in 13.06msec, that is faster than the video rate. The defocused images (near and far-focused) were optically registered for magnification using Telecentric optics. A frequency domain approach based on phase correlation was employed to measure the radial shifts due to magnification and also to optimally position the external aperture. The telecentric optics ensured pixel to pixel registration between the defocused images was correct and provided more accurate depth estimates

Finger Vein Image Deblurring Using Neighbors-Based Binary-GAN (NB-GAN)

Vein contraction and venous compression typically caused by low temperature and excessive placement pressure can blur the captured finger vein images and severely impair the quality of extracted features. To improve the quality of captured finger vein image, this paper proposes a 26-layer generator network constrained by Neighbors-based Binary Patterns (NBP) texture loss to recover the clear image (guessing the original clear image). Firstly, by analyzing various types and degrees of blurred finger vein images captured in real application scenarios, a method to mathematically model the local and global blurriness using a pair of defocused and mean blur kernels is proposed. By iteratively and alternatively convoluting clear images with both kernels in a multi-scale window, a polymorphic blur training set is constructed for network training. Then, NBP texture loss is used for training the generator to enhance the deblurring ability of the network on images. Lastly, a novel network structure is proposed to retain more vein texture feature information, and two residual connections are added on both sides of the residual module of the 26-layer generator network to prevent degradation and overfitting. Theoretical analysis and simulation results show that the proposed neighbors-based binary-GAN (NB-GAN) can achieve better deblurring performance than the the-state-of-the-art approaches

Multi-focus image fusion using maximum symmetric surround saliency detection

In digital photography, two or more objects of a scene cannot be focused at the same time. If we focus one object, we may lose information about other objects and vice versa. Multi-focus image fusion is a process of generating an all-in-focus image from several out-of-focus images. In this paper, we propose a new multi-focus image fusion method based on two-scale image decomposition and saliency detection using maximum symmetric surround. This method is very beneficial because the saliency map used in this method can highlight the saliency information present in the source images with well defined boundaries. A weight map construction method based on saliency information is developed in this paper. This weight map can identify the focus and defocus regions present in the image very well. So we implemented a new fusion algorithm based on weight map which integrate only focused region information into the fused image. Unlike multi-scale image fusion methods, in this method two-scale image decomposition is sufficient. So, it is computationally efficient. Proposed method is tested on several multi-focus image datasets and it is compared with traditional and recently proposed fusion methods using various fusion metrics. Results justify that our proposed method gives stable and promising performance when compared to that of the existing methods

Algorithmic Information Theory Applications in Bright Field Microscopy and Epithelial Pattern Formation

Algorithmic Information Theory (AIT), also known as Kolmogorov complexity, is a quantitative approach to defining information. AIT is mainly used to measure the amount of information present in the observations of a given phenomenon. In this dissertation we explore the applications of AIT in two case studies. The first examines bright field cell image segmentation and the second examines the information complexity of multicellular patterns. In the first study we demonstrate that our proposed AIT-based algorithm provides an accurate and robust bright field cell segmentation. Cell segmentation is the process of detecting cells in microscopy images, which is usually a challenging task for bright field microscopy due to the low contrast of the images. In the second study, which is the primary contribution of this dissertation, we employ an AIT-based algorithm to quantify the complexity of information content that arises during the development of multicellular organisms. We simulate multicellular organism development by coupling the Gene Regulatory Networks (GRN) within an epithelial field. Our results show that the configuration of GRNs influences the information complexity in the resultant multicellular patterns

Modeling and applications of the focus cue in conventional digital cameras

El enfoque en cámaras digitales juega un papel fundamental tanto en la calidad de la imagen como en la percepción del entorno. Esta tesis estudia el enfoque en cámaras digitales convencionales, tales como cámaras de móviles, fotográficas, webcams y similares. Una revisión rigurosa de los conceptos teóricos detras del enfoque en cámaras convencionales muestra que, a pasar de su utilidad, el modelo clásico del thin lens presenta muchas limitaciones para aplicación en diferentes problemas relacionados con el foco. En esta tesis, el focus profile es propuesto como una alternativa a conceptos clásicos como la profundidad de campo. Los nuevos conceptos introducidos en esta tesis son aplicados a diferentes problemas relacionados con el foco, tales como la adquisición eficiente de imágenes, estimación de profundidad, integración de elementos perceptuales y fusión de imágenes. Los resultados experimentales muestran la aplicación exitosa de los modelos propuestos.The focus of digital cameras plays a fundamental role in both the quality of the acquired images and the perception of the imaged scene. This thesis studies the focus cue in conventional cameras with focus control, such as cellphone cameras, photography cameras, webcams and the like. A deep review of the theoretical concepts behind focus in conventional cameras reveals that, despite its usefulness, the widely known thin lens model has several limitations for solving different focus-related problems in computer vision. In order to overcome these limitations, the focus profile model is introduced as an alternative to classic concepts, such as the near and far limits of the depth-of-field. The new concepts introduced in this dissertation are exploited for solving diverse focus-related problems, such as efficient image capture, depth estimation, visual cue integration and image fusion. The results obtained through an exhaustive experimental validation demonstrate the applicability of the proposed models

Accurate depth from defocus estimation with video-rate implementation

The science of measuring depth from images at video rate using „defocus‟ has been investigated. The method required two differently focussed images acquired from a single view point using a single camera. The relative blur between the images was used to determine the in-focus axial points of each pixel and hence depth. The depth estimation algorithm researched by Watanabe and Nayar was employed to recover the depth estimates, but the broadband filters, referred as the Rational filters were designed using a new procedure: the Two Step Polynomial Approach. The filters designed by the new model were largely insensitive to object texture and were shown to model the blur more precisely than the previous method. Experiments with real planar images demonstrated a maximum RMS depth error of 1.18% for the proposed filters, compared to 1.54% for the previous design. The researched software program required five 2D convolutions to be processed in parallel and these convolutions were effectively implemented on a FPGA using a two channel, five stage pipelined architecture, however the precision of the filter coefficients and the variables had to be limited within the processor. The number of multipliers required for each convolution was reduced from 49 to 10 (79.5% reduction) using a Triangular design procedure. Experimental results suggested that the pipelined processor provided depth estimates comparable in accuracy to the full precision Matlab‟s output, and generated depth maps of size 400 x 400 pixels in 13.06msec, that is faster than the video rate. The defocused images (near and far-focused) were optically registered for magnification using Telecentric optics. A frequency domain approach based on phase correlation was employed to measure the radial shifts due to magnification and also to optimally position the external aperture. The telecentric optics ensured pixel to pixel registration between the defocused images was correct and provided more accurate depth estimates.EThOS - Electronic Theses Online ServiceUniversity of Warwick (UoW)GBUnited Kingdo

Fast restoration for out-of-focus blurred images of QR code with edge prior information via image sensing.

Out-of-focus blurring of the QR code is very common in mobile Internet systems, which often causes failure of authentication as a result of a misreading of the information hence adversely affects the operation of the system. To tackle this difficulty, this work firstly introduced an edge prior information, which is the average distance between the center point and the edge of the clear QR code images in the same batch. It is motivated by the theoretical analysis and the practical observation of the theory of CMOS image sensing, optics information, blur invariants, and the invariance of the center of the diffuse light spots. After obtaining the edge prior information, combining the iterative image and the center point of the binary image, the proposed method can accurately estimate the parameter of the out-of-focus blur kernel. Furthermore, we obtain the sharp image by Wiener filter, a non-blind image deblurring algorithm. By this, it avoids excessive redundant calculations. Experimental results validate that the proposed method has great practical utility in terms of deblurring quality, robustness, and computational efficiency, which is suitable for barcode application systems, e.g., warehouse, logistics, and automated production

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition