In the paper is presented one novel approach for adaptive compression of groups of computer tomography (CT) images, based on modified branched inverse pyramid decomposition. In result is obtained high compression ratio with retained image quality. To achieve this, was used the high correlation between sequences of CT images, representing same object(s). The experimental results confirm the effi...

Just as image data compression is designed to save space while preserving the essence of an image, we present an adaptive pyramidal rendering scheme designed to save rendering time while maintaining acceptable image quality. Our coarse-to-fine scheme predicts when and where it is safe to take less than one sample per output pixel, and exploits spatial redundancy to predict pixel colors in the r...

We present a novel hybrid scheme called “hyper-resolution” that integrates image probability-filtering-based interpolation and improved Poisson maximum a posteriori (MAP) super-resolution to respectively enhance high spatial and spatial-frequency resolutions of a single down-sampled image. A new approach to interpolation is proposed for simultaneous image interpolation and smoothing by exploiti...

Just as image data compression is designed to save space while preserving the essence of an image, we present an adaptive pyramidal rendering scheme designed to save rendering time while maintaining acceptable image quality. Our coarse-to-fine scheme predicts when and where it is safe to take less than one sample per output pixel, and exploits spatial redundancy to predict pixel colors in the r...

This paper presents a new method for image compression by neural networks. First, we show that we can use neural networks in a pyramidal framework, yielding the so-called PCA pyramids. Then we present an image compression method based on the PCA pyramid, which is similar to the Laplace pyramid and wavelet transform. Some experimental results with real images are reported. Finally, we present a ...

| This paper is concerned with hierarchical Markov Random Field (MRF) models and their application to sonar image segmentation. We present an original hierarchical seg-mentation procedure devoted to images given by a high resolution sonar. The sonar image is segmented into two kinds of regions: shadow (corresponding to a lack of acoustic reverberation behind each object lying on the sea-bed) an...

A novel three-dimensional gray-level interpolation method called the Directional Coherence Interpolation (DCI) is presented in the paper. The principal advantage of the proposed approach is that it leads to significantly higher visual quality in 3D rendering when compared with traditional image interpolation methods. The basis of DCI is a form of directional image-space coherence. DCI interpola...

image segmentation is an important task in image processing and computer vision which attract many researchers attention. there are a couple of information sets pixels in an image: statistical and structural information which refer to the feature value of pixel data and local correlation of pixel data, respectively. markov random field (mrf) is a tool for modeling statistical and structural inf...

Recently, several wavelet transform based image coding techniques are reported in literature. Though these schemes yield very low bit rates, they suffer from the drawback of computational complexity. There is always a need for simple, fast and easy to implement image coding techniques that yield good subjective quality at low bitrates. To obtain good data compression, the wavelet transform coef...

This paper is concerned with hierarchical Markov random field (MRP) models and their application to sonar image segmentation. We present an original hierarchical segmentation procedure devoted to images given by a high-resolution sonar. The sonar image is segmented into two kinds of regions: shadow (corresponding to a lack of acoustic reverberation behind each object lying on the sea-bed) and s...