Retinex theory for color image enhancement: A systematic review

A short but comprehensive review of Retinex has been presented in this paper. Retinex theory aims to explain human color perception. In addition, its derivation on modifying the reflectance components has introduced effective approaches for images contrast enhancement. In this review, the classical theory of Retinex has been covered. Moreover, advance and improved techniques of Retinex, proposed in the literature, have been addressed. Strength and weakness aspects of each technique are discussed and compared. An optimum parameter is needed to be determined to define the image degradation level. Such parameter determination would help in quantifying the amount of adjustment in the Retinex theory. Thus, a robust framework to modify the reflectance component of the Retinex theory can be developed to enhance the overall quality of color images

Fusion of Visual and Thermal Images Using Genetic Algorithms

Demands for reliable person identification systems have increased significantly due to highly security risks in our daily life. Recently, person identification systems are built upon the biometrics techniques such as face recognition. Although face recognition systems have reached a certain level of maturity, their accomplishments in practical applications are restricted by some challenges, such as illumination variations. Current visual face recognition systems perform relatively well under controlled illumination conditions while thermal face recognition systems are more advantageous for detecting disguised faces or when there is no illumination control. A hybrid system utilizing both visual and thermal images for face recognition will be beneficial. The overall goal of this research is to develop computational methods that improve image quality by fusing visual and thermal face images. First, three novel algorithms were proposed to enhance visual face images. In those techniques, specifical nonlinear image transfer functions were developed and parameters associated with the functions were determined by image statistics, making the algorithms adaptive. Second, methods were developed for registering the enhanced visual images to their corresponding thermal images. Landmarks in the images were first detected and a subset of those landmarks were selected to compute a transformation matrix for the registration. Finally, A Genetic algorithm was proposed to fuse the registered visual and thermal images. Experimental results showed that image quality can be significantly improved using the proposed framework

Low-Light Enhancement in the Frequency Domain

Decreased visibility, intensive noise, and biased color are the common problems existing in low-light images. These visual disturbances further reduce the performance of high-level vision tasks, such as object detection, and tracking. To address this issue, some image enhancement methods have been proposed to increase the image contrast. However, most of them are implemented only in the spatial domain, which can be severely influenced by noise signals while enhancing. Hence, in this work, we propose a novel residual recurrent multi-wavelet convolutional neural network R2-MWCNN learned in the frequency domain that can simultaneously increase the image contrast and reduce noise signals well. This end-to-end trainable network utilizes a multi-level discrete wavelet transform to divide input feature maps into distinct frequencies, resulting in a better denoise impact. A channel-wise loss function is proposed to correct the color distortion for more realistic results. Extensive experiments demonstrate that our proposed R2-MWCNN outperforms the state-of-the-art methods quantitively and qualitatively.Comment: 8 page

Learning as a Nonlinear Line of Attraction for Pattern Association, Classification and Recognition

Development of a mathematical model for learning a nonlinear line of attraction is presented in this dissertation, in contrast to the conventional recurrent neural network model in which the memory is stored in an attractive fixed point at discrete location in state space. A nonlinear line of attraction is the encapsulation of attractive fixed points scattered in state space as an attractive nonlinear line, describing patterns with similar characteristics as a family of patterns. It is usually of prime imperative to guarantee the convergence of the dynamics of the recurrent network for associative learning and recall. We propose to alter this picture. That is, if the brain remembers by converging to the state representing familiar patterns, it should also diverge from such states when presented by an unknown encoded representation of a visual image. The conception of the dynamics of the nonlinear line attractor network to operate between stable and unstable states is the second contribution in this dissertation research. These criteria can be used to circumvent the plasticity-stability dilemma by using the unstable state as an indicator to create a new line for an unfamiliar pattern. This novel learning strategy utilizes stability (convergence) and instability (divergence) criteria of the designed dynamics to induce self-organizing behavior. The self-organizing behavior of the nonlinear line attractor model can manifest complex dynamics in an unsupervised manner. The third contribution of this dissertation is the introduction of the concept of manifold of color perception. The fourth contribution of this dissertation is the development of a nonlinear dimensionality reduction technique by embedding a set of related observations into a low-dimensional space utilizing the result attained by the learned memory matrices of the nonlinear line attractor network. Development of a system for affective states computation is also presented in this dissertation. This system is capable of extracting the user\u27s mental state in real time using a low cost computer. It is successfully interfaced with an advanced learning environment for human-computer interaction

Signals and Images in Sea Technologies

Life below water is the 14th Sustainable Development Goal (SDG) envisaged by the United Nations and is aimed at conserving and sustainably using the oceans, seas, and marine resources for sustainable development. It is not difficult to argue that signals and image technologies may play an essential role in achieving the foreseen targets linked to SDG 14. Besides increasing the general knowledge of ocean health by means of data analysis, methodologies based on signal and image processing can be helpful in environmental monitoring, in protecting and restoring ecosystems, in finding new sensor technologies for green routing and eco-friendly ships, in providing tools for implementing best practices for sustainable fishing, as well as in defining frameworks and intelligent systems for enforcing sea law and making the sea a safer and more secure place. Imaging is also a key element for the exploration of the underwater world for various scopes, ranging from the predictive maintenance of sub-sea pipelines and other infrastructure projects, to the discovery, documentation, and protection of sunken cultural heritage. The scope of this Special Issue encompasses investigations into techniques and ICT approaches and, in particular, the study and application of signal- and image-based methods and, in turn, exploration of the advantages of their application in the previously mentioned areas

Design of a High-Speed Architecture for Stabilization of Video Captured Under Non-Uniform Lighting Conditions

Video captured in shaky conditions may lead to vibrations. A robust algorithm to immobilize the video by compensating for the vibrations from physical settings of the camera is presented in this dissertation. A very high performance hardware architecture on Field Programmable Gate Array (FPGA) technology is also developed for the implementation of the stabilization system. Stabilization of video sequences captured under non-uniform lighting conditions begins with a nonlinear enhancement process. This improves the visibility of the scene captured from physical sensing devices which have limited dynamic range. This physical limitation causes the saturated region of the image to shadow out the rest of the scene. It is therefore desirable to bring back a more uniform scene which eliminates the shadows to a certain extent. Stabilization of video requires the estimation of global motion parameters. By obtaining reliable background motion, the video can be spatially transformed to the reference sequence thereby eliminating the unintended motion of the camera. A reflectance-illuminance model for video enhancement is used in this research work to improve the visibility and quality of the scene. With fast color space conversion, the computational complexity is reduced to a minimum. The basic video stabilization model is formulated and configured for hardware implementation. Such a model involves evaluation of reliable features for tracking, motion estimation, and affine transformation to map the display coordinates of a stabilized sequence. The multiplications, divisions and exponentiations are replaced by simple arithmetic and logic operations using improved log-domain computations in the hardware modules. On Xilinx\u27s Virtex II 2V8000-5 FPGA platform, the prototype system consumes 59% logic slices, 30% flip-flops, 34% lookup tables, 35% embedded RAMs and two ZBT frame buffers. The system is capable of rendering 180.9 million pixels per second (mpps) and consumes approximately 30.6 watts of power at 1.5 volts. With a 1024×1024 frame, the throughput is equivalent to 172 frames per second (fps). Future work will optimize the performance-resource trade-off to meet the specific needs of the applications. It further extends the model for extraction and tracking of moving objects as our model inherently encapsulates the attributes of spatial distortion and motion prediction to reduce complexity. With these parameters to narrow down the processing range, it is possible to achieve a minimum of 20 fps on desktop computers with Intel Core 2 Duo or Quad Core CPUs and 2GB DDR2 memory without a dedicated hardware

Kuadratik görüntü filtrelerinin hızlandırılmış eğitimi için GPU tabanlı yeni bir algoritma tasarımı

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Kuadratik görüntü filtreleri, doğrusal olmayan gürültü karakteristiklerini ele almada genellikle doğrusal filtrelere göre daha başarılıdır. Ancak, ikinci dereceden filtrelerin başarısı için uygun ağırlıkların belirlenmesi, doğrusal filtrelere ağırlıklarının belirlenmesi gibi kolay değildir. Bununla birlikte referans ve gürültülü görüntülerin bulunduğu durumlarda filtre ağırlıkları optimizasyon yöntemleri ile belirlenebilir. Sezgisel algoritmalar kullanılarak ağırlıkların belirlenmesi için, referans görüntü ile filtrelenmiş görüntüye dayanan bir uygunluk değeri (Fitness Value) hesaplanması gerekir. Hesaplanan bu uygunluk değerine göre maske ağırlıkları güncellenmektedir. Bu durum durdurma kriterine kadar yinelemeli olarak tekrarlanır ve maske ağırlıklarının uygun değerleri hesaplanır. Bu süreçler çarpma işlemleri yoğun olan Kuadratik filtrelerde çok zaman almaktadır. Bu tez çalışması kapsamında Kuadratik görüntü filtrelerinin maske eğitimlerini hızlandırmak için algoritmik hızlandırma, donanımsal hızlandırma ve bunların birlikte kullanıldığı yöntemler geliştirilmiştir. Kuadratik görüntü filtrelerin maske ağırlıklarının belirlenmesi için literatürde sıklıkla başvurulan sezgisel algoritmalardan, GA (Genetik Algoritmalar) ve PSO (Parçacık Sürü Optimizasyon) Algoritmaları kullanılmıştır. Deneysel çalışmalarda, referans görüntü üzerine Gaussian gürültüsü eklenerek gürültülü test görüntüleri elde edilmiştir. Uygunluk değeri, referans görüntü ve filtrelenmiş görüntü kullanılarak belirlenen hata fonksiyonundan hesaplanmıştır. Yapılan deneysel çalışmalardan elde edilen sonuçlara göre hem algoritmik hem de donanımsal hızlandırma için önerilen yöntemlerin eğitim sürelerini kısaltmada başarılı olduğu görülmüştür. Elde edilen karşılaştırmalı sayısal sonuçlara göre, GPU hızlandırma ile birlikte kullanılan algoritmik hızlandırmada eğitim süreleri ortalama 300 kat daha fazla kısalmıştır. Ayrıca görüntü kaliteleri de sıralı gerçekleştirme ile elde edilen görüntü kalitelerine benzer oranda elde edilmiştir.Quadratic image filters are usually more successful than linear filters in handling nonlinear noise characteristics. However, determining the appropriate weights for the success of the second order filters is not as straightforward as determining the weights of the linear filters. On the other hand, when there are reference and noisy images, the filter weights can be determined by optimization methods. To determine the weights using heuristic algorithms, a fitness value based on the reference image and the filtered image must be calculated. The mask weights are updated according to this calculated fitness value. This is iteratively repeated until the stopping criterion and the appropriate values of the mask weights are calculated. These processes take a lot of time in Quadratic filters, which are intensive processes of multiplication. Within the scope of this thesis, algorithmic acceleration, hardware acceleration and methods used together to develop mask training of quadratic image filters have been developed. GA (Genetic Algorithms) and PSO (Particle Swarm Optimization) Algorithms have been used for the heuristic algorithms frequently used in the literature for determining mask weights of quadratic image filters. In experimental studies, noisy test images were obtained by adding a Gaussian noise to the reference image. The fitness value is calculated from the error function determined using the reference image and the filtered image. According to the results obtained from the experimental studies, it is seen that the methods proposed for both algorithmic and hardware acceleration are successful in shortening the training periods. According to the comparative numerical results obtained, the training times were shortened by an average of 300 times in the algorithmic acceleration used with GPU acceleration. In addition, image qualities are similar to those obtained by sequential realization

Deep learning-based diagnostic system for malignant liver detection

Cancer is the second most common cause of death of human beings, whereas liver cancer is the fifth most common cause of mortality. The prevention of deadly diseases in living beings requires timely, independent, accurate, and robust detection of ailment by a computer-aided diagnostic (CAD) system. Executing such intelligent CAD requires some preliminary steps, including preprocessing, attribute analysis, and identification. In recent studies, conventional techniques have been used to develop computer-aided diagnosis algorithms. However, such traditional methods could immensely affect the structural properties of processed images with inconsistent performance due to variable shape and size of region-of-interest. Moreover, the unavailability of sufficient datasets makes the performance of the proposed methods doubtful for commercial use. To address these limitations, I propose novel methodologies in this dissertation. First, I modified a generative adversarial network to perform deblurring and contrast adjustment on computed tomography (CT) scans. Second, I designed a deep neural network with a novel loss function for fully automatic precise segmentation of liver and lesions from CT scans. Third, I developed a multi-modal deep neural network to integrate pathological data with imaging data to perform computer-aided diagnosis for malignant liver detection. The dissertation starts with background information that discusses the proposed study objectives and the workflow. Afterward, Chapter 2 reviews a general schematic for developing a computer-aided algorithm, including image acquisition techniques, preprocessing steps, feature extraction approaches, and machine learning-based prediction methods. The first study proposed in Chapter 3 discusses blurred images and their possible effects on classification. A novel multi-scale GAN network with residual image learning is proposed to deblur images. The second method in Chapter 4 addresses the issue of low-contrast CT scan images. A multi-level GAN is utilized to enhance images with well-contrast regions. Thus, the enhanced images improve the cancer diagnosis performance. Chapter 5 proposes a deep neural network for the segmentation of liver and lesions from abdominal CT scan images. A modified Unet with a novel loss function can precisely segment minute lesions. Similarly, Chapter 6 introduces a multi-modal approach for liver cancer variants diagnosis. The pathological data are integrated with CT scan images to diagnose liver cancer variants. In summary, this dissertation presents novel algorithms for preprocessing and disease detection. Furthermore, the comparative analysis validates the effectiveness of proposed methods in computer-aided diagnosis