Catálogo de publicaciones - libros

When the reconstructed image of the base layer is up-sampled for decoding the enhancement layer of spatial scalability, direction information derived during decoding the spatially lower layer is used. That is direction information used for Intra prediction which is used for up-sampling again. In most cases, it shows 0.1-0.5dB quality improvement in images up-sampled by using directional filtering compared to those up-sampled conventionally. The same interpolation algorithm should be used in both the encoder and decoder.

This paper focuses on fuzzy image denoising techniques. In particular, we investigate the usage of fuzzy set theory in the domain of image enhancement using wavelet thresholding. We propose a simple but efficient new fuzzy wavelet shrinkage method, which can be seen as a fuzzy variant of a recently published probabilistic shrinkage method [1] for reducing adaptive Gaussian noise from digital greyscale images. Experimental results show that the proposed method can efficiently and rapidly remove additive Gaussian noise from digital greyscale images. Numerical and visual observations show that the performance of the proposed method outperforms current fuzzy non-wavelet methods and is comparable with some recent but more complex wavelets methods. We also illustrate the main differences between this version and the probabilistic version and show the main improvements in comparison to it.

In this paper we adapt different techniques for image deconvolution, to the actual restoration of works of arts (mainly paintings and sculptures) from the baroque period. We use the special characteristics of these works in order to both restrict the strategies and benefit from those properties.

We propose an algorithm which presents good results in the pieces we have worked. Due to the diversity of the period and the amount of artists who made it possible, the algorithms are too general even in this context. This is a first approach to the problem, in which we have assumed very common and shared features for the works of art. The flexibility of the algorithm, and the freedom to choose some parameters make it possible to adapt the problem to the knowledge that restorators in charge may have about a particular work.

This paper deals with concealment of motion blur in image sequences. The approach is different from traditional methods, which attempt to deblur the image. Our approach utilizes the information in consecutive frames, replacing blurred areas of the images with corresponding sharp areas from the previous frames. Blurred but otherwise unchanged areas of the images are recognized using blur invariant features. A statistical approach for calculating the weights for the blur invariant features in frequency and spatial domains is also proposed, and compared to the unweighted invariants in an ideal setting. Finally, the performance of the method is tested using a real blurred image sequence. The results support the use of our approach with the weighting scheme.

A standard sigma filter proposed by J.-S. Lee has found wide applications and frequent implementations in software packages. Later, several modifications have been introduced in order to improve its performance. In this paper we propose some new modifications trying to combine advantages of the original sigma and local statistic Lee filter as well as to ensure the filter robustness with respect to impulse noise. The basic performance characteristics of the proposed hybrid sigma filter are studied for cases of pure multiplicative noise. The comparison to some other well known filters is performed. A real life example of the designed filter application to side-look radar image is given.

This paper presents a method for automatic removal of local defects such as blotches and impulse noise in old motion picture films. The method is fully automatic and includes the following steps: fuzzy prefiltering, motion-compensated blotch detection, and spatiotemporal inpainting. The fuzzy prefilter removes small defective areas such as impulse noise. Modified bidirectional motion estimation with a predictive diamond search is utilized to estimate the motion vectors. The blotches are detected by the rank-ordered-difference method. Detected missing regions are interpolated by a new exemplar-based inpainting approach that operates on three successive frames. The performance of the proposed method is demonstrated on an artificially corrupted image sequence and on a real motion picture film. The results of the experiments show that the proposed method efficiently removes flashing and still blotches and impulse noise from image sequences.

We present a novel dedicated hardware system for the extraction of second-order statistical features from high-resolution images. The selected features are based on gray level co-occurrence matrix analysis and are angular second moment, correlation, inverse difference moment and entropy. The proposed system was evaluated using input images with resolutions that range from 512(512 to 2048(2048 pixels. Each image is divided into blocks of user-defined size and a feature vector is extracted for each block. The system is implemented on a Xilinx VirtexE-2000 FPGA and uses integer arithmetic, a sparse co-occurrence matrix representation and a fast logarithm approximation to improve efficiency. It allows the parallel calculation of sixteen co-occurrence matrices and four feature vectors on the same FPGA core. The experimental results illustrate the feasibility of real-time feature extraction for input images of dimensions up to 2048(2048 pixels, where a performance of 32 images per second is achieved.

When magnifying a bitmapped image, we want to increase the number of pixels it covers, allowing for finer details in the image, which are not visible in the original image. Simple interpolation techniques are not suitable because they introduce jagged edges, also called “jaggies”.

Earlier we proposed the “mm” magnification method (for integer scaling factors), which avoids jaggies. It is based on mathematical morphology. The algorithm detects jaggies in magnified binary images (using pixel replication) and removes them, making the edges smoother. This is done by replacing the value of specific pixels.

In this paper, we extend the binary mm to greyscale images. The pixels are locally binarized so that the same morphological techniques can be applied as for mm. We take care of the more difficult replacement of pixel values, because several grey values can be part of a jaggy. We then discuss the visual results of the new greyscale method.

For nonseparable bidimensional wavelet transforms, the choice of the dilation matrix is all–important, since it governs the downsampling and upsampling steps, determines the cosets that give the positions of the filters, and defines the elementary set that gives a tesselation of the plane. We introduce nonseparable bidimensional wavelets, and give formulae for the analysis and synthesis of images. We analyze several dilation matrices, and show how the wavelet transform operates visually. We also show some distorsions produced by some of these matrices. We show that the requirement of their eigenvalues being greater than 1 in absolute value is not enough to guarantee their suitability for image processing applications, and discuss other conditions.

A new evolutionary approach for construction of uniform impulse noise filter is presented. Genetic programming is used for combining the basic image transformations and filters into tree structure, which can accurately estimate noise map. Proposed detector is employed for building switching-scheme filter, where recursively implemented -trimmed mean is used as the estimator of corrupted pixel values. The proposed evolutionary filtering structure shows very good results in removal of uniform impulse noise, for wide range of noise probabilities and different test images.