Bilateral filter in image processing

The bilateral filter is a nonlinear filter that does spatial averaging without smoothing edges. It has shown to be an effective image denoising technique. It also can be applied to the blocking artifacts reduction. An important issue with the application of the bilateral filter is the selection of the filter parameters, which affect the results significantly. Another research interest of bilateral filter is acceleration of the computation speed. There are three main contributions of this thesis. The first contribution is an empirical study of the optimal bilateral filter parameter selection in image denoising. I propose an extension of the bilateral filter: multi resolution bilateral filter, where bilateral filtering is applied to the low-frequency sub-bands of a signal decomposed using a wavelet filter bank. The multi resolution bilateral filter is combined with wavelet thresholding to form a new image denoising framework, which turns out to be very effective in eliminating noise in real noisy images. The second contribution is that I present a spatially adaptive method to reduce compression artifacts. To avoid over-smoothing texture regions and to effectively eliminate blocking and ringing artifacts, in this paper, texture regions and block boundary discontinuities are first detected; these are then used to control/adapt the spatial and intensity parameters of the bilateral filter. The test results prove that the adaptive method can improve the quality of restored images significantly better than the standard bilateral filter. The third contribution is the improvement of the fast bilateral filter, in which I use a combination of multi windows to approximate the Gaussian filter more precisely

Video enhancement : content classification and model selection

The purpose of video enhancement is to improve the subjective picture quality. The field of video enhancement includes a broad category of research topics, such as removing noise in the video, highlighting some specified features and improving the appearance or visibility of the video content. The common difficulty in this field is how to make images or videos more beautiful, or subjectively better. Traditional approaches involve lots of iterations between subjective assessment experiments and redesigns of algorithm improvements, which are very time consuming. Researchers have attempted to design a video quality metric to replace the subjective assessment, but so far it is not successful. As a way to avoid heuristics in the enhancement algorithm design, least mean square methods have received considerable attention. They can optimize filter coefficients automatically by minimizing the difference between processed videos and desired versions through a training. However, these methods are only optimal on average but not locally. To solve the problem, one can apply the least mean square optimization for individual categories that are classified by local image content. The most interesting example is Kondo’s concept of local content adaptivity for image interpolation, which we found could be generalized into an ideal framework for content adaptive video processing. We identify two parts in the concept, content classification and adaptive processing. By exploring new classifiers for the content classification and new models for the adaptive processing, we have generalized a framework for more enhancement applications. For the part of content classification, new classifiers have been proposed to classify different image degradations such as coding artifacts and focal blur. For the coding artifact, a novel classifier has been proposed based on the combination of local structure and contrast, which does not require coding block grid detection. For the focal blur, we have proposed a novel local blur estimation method based on edges, which does not require edge orientation detection and shows more robust blur estimation. With these classifiers, the proposed framework has been extended to coding artifact robust enhancement and blur dependant enhancement. With the content adaptivity to more image features, the number of content classes can increase significantly. We show that it is possible to reduce the number of classes without sacrificing much performance. For the part of model selection, we have introduced several nonlinear filters to the proposed framework. We have also proposed a new type of nonlinear filter, trained bilateral filter, which combines both advantages of the original bilateral filter and the least mean square optimization. With these nonlinear filters, the proposed framework show better performance than with linear filters. Furthermore, we have shown a proof-of-concept for a trained approach to obtain contrast enhancement by a supervised learning. The transfer curves are optimized based on the classification of global or local image content. It showed that it is possible to obtain the desired effect by learning from other computationally expensive enhancement algorithms or expert-tuned examples through the trained approach. Looking back, the thesis reveals a single versatile framework for video enhancement applications. It widens the application scope by including new content classifiers and new processing models and offers scalabilities with solutions to reduce the number of classes, which can greatly accelerate the algorithm design

Lymphatic Filariasis detection in microscopic images

In Africa, the propagation of parasites like the lymphatic filariasis is complicatingseriously the efforts of health professionals to cure certain diseases. Although there aremedicines capable to treat the lymphatic filariasis, this needs to be discovered firstly which isnot always an easy task having into account that in most countries affected by this disease it canonly be detected at night (nocturne). The lymphatic filariasis is then, a parasitical infectionwhich can originate changes or ruptures in the lymphatic system as well as an abnormal growthof certain areas of the body causing pain, incapacity and social stigma.Approximately 1.23 billion people in 58 countries from all over the world are threatenedby this disease which requires a preventive treatment to stop its propagation which makes iteven more important for the existence of a mechanism that is less costly and more agile in theanalysis of a blood smear to verify the existence of microfilariae (little worms that are producedby other adult worms while housed in the lymphatic system).The lymphatic filariasis is caused by an infection with nematodes ("roundworms") of theFilariodidea family in which three types are inserted: Wuchereria Bancroft, responsible for 90%of all cases; Brugia Malayi, responsible for almost every remaining; B.Timori also causing thedisease. All three have characteristics that can differentiate them which allow them to beidentified.The current identification process of the disease consists on the analysis of microfilariae ina blood smear with a blood sample through a microscope and its identification by the observer.Taking this into account, it is intended to develop image analysis and processingtechniques for the recognition and counting of the two principal types of filarial worms from athin blood smear, a smartphone and a portable microscope making the detection possiblewithout the need of a health professional and consequent automation of the process. To makethis possible an adapter smartphone-microscope can be used to obtain an image with themagnification of 40x3. The images can then be analyzed in a server or in the smartphone, if ithas enough processing for it. It is expected from this process that the need to resort to labs toprocess the blood smear gets fulfilled making the process more accessible and agile instead ofcostly and slow.For the detection of the parasites from the acquired images it is intended to implement,experiment and choose the more adequate operations. These comprise pre-processing operationswith the goal to enhance the acquired images and eliminate possible artifacts prevenient fromthe acquisition system. However, the principal operations should be those that allow theverification of existence or nonexistence, recognition and classification of the pretendedparasites. Processing and analysis techniques that are common in these processes are based inthe extraction of features (e.g. SIRF, SURF, and FLANN) template similarity, edge detectionand description of contours and recognition of statistical patterns.Once detected and recognized one or more parasites and its types should be defined andused a rule to declare the presence of the disease and its stage

Contributions to reconfigurable video coding and low bit rate video coding

In this PhD Thesis, two different issues on video coding are stated and their corresponding proposed solutions discussed. In the first place, some problems of the use of video coding standards are identi ed and the potential of new reconfigurable platforms is put to the test. Specifically, the proposal from MPEG for a Reconfigurable Video Coding (RVC) standard is compared with a more ambitious proposal for Fully Configurable Video Coding (FCVC). In both cases, the objective is to nd a way for the definition of new video codecs without the concurrence of a classical standardization process, in order to reduce the time-to-market of new ideas while maintaining the proper interoperability between codecs. The main difference between these approaches is the ability of FCVC to reconfigure each program line in the encoder and decoder definition, while RVC only enables to conform the codec description from a database of standardized functional units. The proof of concept carried out in the FCVC prototype enabled to propose the incorporation of some of the FCVC capabilities in future versions of the RVC standard. The second part of the Thesis deals with the design and implementation of a filtering algorithm in a hybrid video encoder in order to simplify the high frequencies present in the prediction residue, which are the most expensive for the encoder in terms of output bit rate. By means of this filtering, the quantization scale employed by the video encoder in low bit rate is kept in reasonable values and the risk of appearance of encoding artifacts is reduced. The proposed algorithm includes a block for filter control that determines the proper amount of filtering from the encoder operating point and the characteristics of the sequence to be processed. This filter control is tuned according to perceptual considerations related with overall subjective quality assessment. Finally, the complete algorithm was tested by means of a standard subjective video quality assessment test, and the results showed a noticeable improvement in the quality score with respect to the non-filtered version, confirming that the proposed method reduces the presence of harmful low bit rate artifacts

Aerial Robotics for Inspection and Maintenance

Aerial robots with perception, navigation, and manipulation capabilities are extending the range of applications of drones, allowing the integration of different sensor devices and robotic manipulators to perform inspection and maintenance operations on infrastructures such as power lines, bridges, viaducts, or walls, involving typically physical interactions on flight. New research and technological challenges arise from applications demanding the benefits of aerial robots, particularly in outdoor environments. This book collects eleven papers from different research groups from Spain, Croatia, Italy, Japan, the USA, the Netherlands, and Denmark, focused on the design, development, and experimental validation of methods and technologies for inspection and maintenance using aerial robots

Adaptive filtering techniques for acquisition noise and coding artifacts of digital pictures

The quality of digital pictures is often degraded by various processes (e.g, acquisition or capturing, compression, filtering process, transmission, etc). In digital image/video processing systems, random noise appearing in images is mainly generated during the capturing process; while the artifacts (or distortions) are generated in compression or filtering processes. This dissertation looks at digital image/video quality degradations with possible solution for post processing techniques for coding artifacts and acquisition noise reduction for images/videos. Three major issues associated with the image/video degradation are addressed in this work. The first issue is the temporal fluctuation artifact in digitally compressed videos. In the state-of-art video coding standard, H.264/AVC, temporal fluctuations are noticeable between intra picture frames or between an intra picture frame and neighbouring inter picture frames. To resolve this problem, a novel robust statistical temporal filtering technique is proposed. It utilises a re-descending robust statistical model with outlier rejection feature to reduce the temporal fluctuations while preserving picture details and motion sharpness. PSNR and sum of square difference (SSD) show improvement of proposed filters over other benchmark filters. Even for videos contain high motion, the proposed temporal filter shows good performances in fluctuation reduction and motion clarity preservation compared with other baseline temporal filters. The second issue concerns both the spatial and temporal artifacts (e.g, blocking, ringing, and temporal fluctuation artifacts) appearing in compressed video. To address this issue, a novel joint spatial and temporal filtering framework is constructed for artifacts reduction. Both the spatial and the temporal filters employ a re-descending robust statistical model (RRSM) in the filtering processes. The robust statistical spatial filter (RSSF) reduces spatial blocking and ringing artifacts whilst the robust statistical temporal filter (RSTF) suppresses the temporal fluctuations. Performance evaluations demonstrate that the proposed joint spatio-temporal filter is superior to H.264 loop filter in terms of spatial and temporal artifacts reduction and motion clarity preservation. The third issue is random noise, commonly modeled as mixed Gaussian and impulse noise (MGIN), which appears in image/video acquisition process. An effective method to estimate MGIN is through a robust estimator, median absolute deviation normalized (MADN). The MADN estimator is used to separate the MGIN model into impulse and additive Gaussian noise portion. Based on this estimation, the proposed filtering process is composed of a modified median filter for impulse noise reduction, and a DCT transform based denoising filter for additive Gaussian noise reduction. However, this DCT based denoising filter produces temporal fluctuations for videos. To solve this problem, a temporal filter is added to the filtering process. Therefore, another joint spatio-temporal filtering scheme is built to achieve the best visual quality of denoised videos. Extensive experiments show that the proposed joint spatio-temporal filtering scheme outperforms other benchmark filters in noise and distortions suppression