Franco Lotti

A Comparison Between Global and Context-Adaptive Pansharpening of Multispectral Images

Multiresolution analysis (MRA) and component substitution (CS) are the two basic frameworks to which image fusion algorithms can be reported when merging multispectral (MS) and panchromatic (Pan) images (pansharpening), acquired with different spatial and spectral resolutions. State-of-the-art algorithms add the spatial details extracted from the Pan into the MS data set by considering different injection strategies. The capability of efficiently modeling the relationships between MS and Pan is crucial for the quality of fusion results and particularly for a correct recovery of local features with a consequent reduction of spectral distortions. Although context-adaptive (CA) injection models have been proposed in the MRA framework, their adoption in CS schemes has been scarcely investigated so far. In this letter, CA strategies are compared with global models by considering a general protocol in which both MRA- and CS-based schemes can be described. Qualitative and quantitative results are reported for three high-resolution data sets from two different sensors, namely, IKONOS and simulated Pleiades. The score gains of well-known and novel quality figures show that CA models are more efficient than global ones.

Lossless image compression by quantization feedback in a content-driven enhanced Laplacian pyramid

In this paper, the effects of quantization noise feedback on the entropy of Laplacian pyramids are investigated. This technique makes it possible for the maximum absolute reconstruction error to be easily and strongly upper-bounded (near-lossless coding), and therefore, allows reversible compression. The entropy-minimizing optimum quantizer is obtained by modeling the first-order distributions of the differential signals as Laplacian densities, and by deriving a model for the equivalent memoryless entropy. A novel approach, based on an enhanced Laplacian pyramid, is proposed for the compression, either lossless or lossy, of gray-scale images. Major details are prioritized through a content-driven decision rule embedded in a uniform threshold quantizer with noise feedback. Lossless coding shows improvements over reversible Joint Photographers Expert Group (JPEG) and the reduced-difference pyramid schemes, while lossy coding outperforms JPEG, with a significant peak signal-to-noise ratio (PSNR) gain. Also, subjective quality is higher even at very low bit rates, due to the absence of the annoying impairments typical of JPEG. Moreover, image versions having resolution and SNR that are both progressively increasing are made available at the receiving end from the earliest retrieval stage on, as intermediate steps of the decoding procedure, without any additional cost.

Principal component analysis of visible and near-infrared multispectral images of works of art

Abstract Principal component analysis (PCA) was applied to a very simple case of a tempera panel painted with four known pigments (cinnabar, malachite, yellow ochre and chromium oxide). The four pigments were spread pure as well as dilute with carbon black (5% w/w, 10% w/w) thus creating 12 homogeneous areas of the same size. The panel was imaged by a Vidicon camera in the visible and near-infrared regions (420–1550 nm) resulting in a set of 29 images. PCA was applied by taking various subsets of the input data. From the analysis of this simple and predictable case study some guidelines are synthesized and proposed for the application to actual work of art. Results are presented for the painted panel. Preliminary results are also reported for the Luca Signorellis “Predella della Trinita”. The multivariate image analysis results in the visible and near-infrared regions show that it is possible to use the multispectral image data in order to get a segmentation and a classification of painted zones by pigments with different chemical composition or physical properties.

Content-driven differential encoding of an enhanced image pyramid

Abstract An adaptive scheme employing pyramid structure is proposed for multiresolution encoding of still pictures. Efficiency is increased by designing a low-entropy pyramid decomposition by means of different reduction/expansion filters, and also by giving encoding priority to important features through a content-driven decision rule. Quantization error feed-back performed along the pyramid levels ensures lossless reconstruction capability and improves the robustness of the algorithm.

A pyramid-based error-bounded encoder: an evaluation on X-ray chest images

Abstract A novel encoder based on an enhanced Laplacian pyramid is proposed for compression of Medical grey-scale images: major details are prioritized through an adaptive decision rule embedded in a uniform threshold quantizer with noise feedback. The major benefit of this content-driven feedback quantizer is that significant features are straightforwardly propagated throughout the pyramid, thus enhancing compactness and visual quality of the reduced-resolution versions progressively associated with the code stream. Nevertheless, the reconstruction error is determined only by the size of the quantization step at the base level of the pyramid, thereby making it possible for the maximum absolute reconstruction error to be easily and strongly upperbounded ( near-lossless compression ), as often required in archiving medical images, for diagnostic and legal purposes. Both lossless and lossy coding show favorable comparisons with JPEG in objective terms, i.e., compression ratios and distortion plots. Lossy coding outperforms JPEG also subjectively, due to the absence of visual impairments and diagnostic artifacts even at very low rates. This feature is also evidenced in a preliminary ROC analysis on a set of X-ray chest images. The pyramid encoder produces compressed images whose diagnostic quality seems to be comparable, for medium rates, to that of the uncompressed versions, and superior to that of the JPEG coded versions.

Variable pyramid structures for image segmentation

Abstract Linked pyramid structures have proved to be a useful tool in digital image processing for many applicatins because of their ability to face problems at different levels of detail. Some variations suggested by usage to existent pyramid algorithms have been investigated for the segmentation of compact objects in noisy IR images. In particular, the efficacy of increasing the span at the very last iterations in order to correct the link deficiency of the boundary nodes is reported. We also report about a method which separate segment roots at any level in the pyramid and mergess the segments under the constraint of the maximum number of regions to be distinguished. The method is applied to IR image segmentation and comparative results are given.

Fiber Optics Reflectance Spectroscopy: A Non-destructive Technique for the Analysis of Works of Art

From the results reported, the FORS device can be considered a useful and non-invasive tool for acquiring spectral information from paintings and wall-paintings in order to identify pigments, to analyze color changes, to monitor the status-of-health, and to detect the presence of alteration products.

Lossless image compression based on recursive nonlinear interpolation

The generalized recursive interpolation (GRINT) algorithm was recently proposed and shown to be the most effective progressive technique for decorrelation of still image. A nonlinear version of GRINT (MRINT) employs median filtering in a nonseparable fashion on a quincunx grid. The main advantage of both these schemes is that interpolation is performed from all error-free values, thereby reducing the variance of interpolation errors. MRINT is embedded in a simplified version of the context-based encoder by Said and Pearlman. Coding performances of the novel context-based coder are evaluated by comparisons with GRINT, and a variety of other multiresolution lossless methods, including the original scheme by Said and Pearlman. The modified scheme outperforms all the other algorithms, including the latter, especially when dealing with medical images.

Content-driven pyramid compression of medical images for error-free archival and progressive transmission.

An efficient encoding scheme based on an improved Laplacian pyramid is proposed for lossless/lossy compression aimed at archival and progressive transmission of grayscale medical images. The major assets are: pyramid entropy is decreased by adopting two different filters for reduction and expansion; encoding priority is given to major details through a hierarchical content-driven decision rule; the binary quad-tree of split/unsplit nodes is blockwise zig-zag scanned and run-length encoded. Error feedback along the levels of the Laplacian pyramid ensures control of the maximum absolute error, up to fully reversible reconstruction capability, and enhances the efficiency and the robustness of the whole scheme. While coding results on the standard greyscale image Lena are pretty competitive with the most recent literature, reversible compression of scanned RX images achieves ratios of about 1:5, establishing improvements over DPCM schemes; high-quality lossy versions at 0.15 bit/pel outperform JPEG both visually and quantitatively. Moreover, no floating-point computation is required in the algorithm, and on-line compression and reconstruction are feasible on general-purpose terminals.

Context-sensitive pan-sharpening of multispectral images

Multiresolution analysis (MRA) and component substitution (CS) are the two basic frameworks to which image fusion algorithms can be reported when merging multi-spectral (MS) and panchromatic (Pan) images (Pan-sharpening). State-of-the-art algorithms add spatial details derived from the Pan image to the MS bands according to an injection model. The capability of the model to describe the relationship between the MS and Pan images is crucial for the quality of fusion results. Although context adaptive (CA) injection models have been proposed in the framework of MRA, their adoption in CS schemes has been scarcely investigated so far. In this work a CA injection model already tested for MRA algorithms is evaluated also for CS schemes. Qualitative and quantitative results reported for IKONOS high spatial resolution data show that CA injection models are more efficient than global ones.