This dissertation addresses the problems of image denoising and compression. image denoising and compression are the fundamental problems in imageprocessing, and often use transform techniques. In this dissertation, ...
This dissertation addresses the problems of image denoising and compression. image denoising and compression are the fundamental problems in imageprocessing, and often use transform techniques. In this dissertation, the wavelet transform is used and new signalprocessing techniques are applied in the wavelet domain. The sparsity of the wavelet transform, i.e., a signal energy is concentrated on few large magnitude coefficients, is a main property exploited by wavelet based imageprocessingapplications. The new methods for image denoising problem introduced in this dissertation exploits the directional structure of the two-dimensional wavelet transform. For image compression, correlations between adjacent scales (levels) and coefficient pixels, i.e., inter and intra-correlation, are adaptively exploited in the proposed algorithm. The most frequently used technique in image denoising problems is a thresholding operation. Thresholding the wavelet coefficients exploits the sparse property of the wavelet transform. Applying thresholding to all coefficients uniformly, however, produces oversmoothing of edges and undersmoothing in uniform regions. Recently, adaptive wavelet thresholding utilizing spatial and adjacent scale correlations has been shown to yield good results. This dissertation introduces a related, but more direct, technique for adaptively processingwavelet coefficients based on partitioning of the coefficient space. In the wavelet domain, the coefficient space is partitioned through vector quantization and mask functions are used to obtain the denoised wavelet coefficients. This approach is better able to exploit structure in the coefficient domain and presented simulations show that the proposed technique yields superior performance compared with current wavelet denoising methods. A new embedded waveletimage compression method, using quad-partition-based wavelet domain image compression, is also proposed. The introduced method uses a quad-partition-base
25 years after the seminal work of Jean Morlet, the wavelet transform, multiresolution analysis, and other space frequency or space scale approaches are considered standard tools by researchers in imageprocessing, an...
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(纸本)9780819464514
25 years after the seminal work of Jean Morlet, the wavelet transform, multiresolution analysis, and other space frequency or space scale approaches are considered standard tools by researchers in imageprocessing, and many applications have been proposed that point out the interest of these techniques. This paper proposes a review of the recent published works dealing with industrial applications of wavelet and, more generally speaking, multiresolution analysis. More than 180 recent papers are presented.
Geological hazards in densely vegetated mountainous regions are challenging to detect due to terrain concealment and the limitations of traditional visualization methods. This study introduces the LiDAR image highligh...
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Geological hazards in densely vegetated mountainous regions are challenging to detect due to terrain concealment and the limitations of traditional visualization methods. This study introduces the LiDAR image highlighting algorithm (LIHA), a novel approach for enhancing micro-topographical features in digital elevation models (DEMs) derived from airborne LiDAR data. By analogizing terrain profiles to non-stationary spectral signals, LIHA applies locally estimated scatterplot smoothing (Loess smoothing), wavelet decomposition, and high-frequency component amplification to emphasize subtle features such as landslide boundaries, cracks, and gullies. The algorithm was validated using the Mengu landslide case study, where edge detection analysis revealed a 20-fold increase in identified micro-topographical features (from 1907 to 37,452) after enhancement. Quantitative evaluation demonstrated LIHA's effectiveness in improving both human interpretation and automated detection accuracy. The results highlight LIHA's potential to advance early geological hazard identification and mitigation, particularly when integrated with machine learning for future applications. This work bridges signalprocessing and geospatial analysis, offering a reproducible framework for high-precision terrain feature extraction in complex environments.
Digital watermarking is crucial for protecting data integrity and intellectual property, especially in medical imaging, where authenticity and privacy are critical. Embedding hidden information within images aids auth...
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Underwater optical imaging is affected by light absorption and backscattering in water, thus yielding low signal-to-noise ratios and limited imaging ranges. This study proposes an image preprocessing method for underw...
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Underwater optical imaging is affected by light absorption and backscattering in water, thus yielding low signal-to-noise ratios and limited imaging ranges. This study proposes an image preprocessing method for underwater, time-gated, single-photon avalanche diode (TG-SPAD)-array-based images according to the Retinex theory, and a block-matching and 3D filtering (BM3D) algorithm to address uneven illumination and complex noise issues in small-diameter, light beam, underwater imaging. Specifically, images undergo rapid illumination correction in combination with time-domain transformation. Subsequently, the proposed method employs the BM3D algorithm in conjunction with an adaptive noise-fitted model and an improved Anscombe transform for denoising. The experimental results demonstrate that the proposed method outperforms various existing image preprocessing techniques in both subjective visual assessments and objective evaluation metrics. The proposed method considerably enhances the visual quality of TG-SPAD-array-based images and is well-suited for underwater, single-photon imaging applications and for the optimized processing of high-precision, 3D depth maps.
The topics included are mathematical developments;transient detection in biological systems;frames and overcomplete representations;image coding;wavelet design and construction;receiver design and demodulation;Gabor t...
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ISBN:
(纸本)0819416274
The topics included are mathematical developments;transient detection in biological systems;frames and overcomplete representations;image coding;wavelet design and construction;receiver design and demodulation;Gabor transforms;high speed processing;detecting chaotic signals and denoising;image compression;multiscale representations;medical imaging;segmentation and classification;signal filtering;feature extraction;and motion estimation.
The proceedings contain 56 papers. The topics discussed include: review of recent results on optimal orthonormal subband coders;comparison of waveletimage coding schemes for seismic data compression;image quality mea...
The proceedings contain 56 papers. The topics discussed include: review of recent results on optimal orthonormal subband coders;comparison of waveletimage coding schemes for seismic data compression;image quality measurement using the Haar wavelet;lossless image compression using wavelets over finite rings and related architectures;on consistent signal reconstruction from wavelet extrema representation;seismic imaging in wavelet domain: decomposition and compression of imaging operator;application of differential mapping and wavelet transform;usage of short wavelets for scalable audio coding;enhanced resolution control for video sequences;regularized multiresolution methods for astronomical image enhancement;weighted time-frequency and time-scale transforms for non-stationary signal detection;and a wavelet detector for distributed objects.
The proceedings contain 93 papers. The topics discussed include: local adaptive image restoration and enhancement with the use of DFT and DCT in a running window;wavelet-based interpolation method for nonuniformly sam...
The proceedings contain 93 papers. The topics discussed include: local adaptive image restoration and enhancement with the use of DFT and DCT in a running window;wavelet-based interpolation method for nonuniformly sampled signals;regularization constraints in lossy compressed astronomical image restoration;lattice structure for multifilters derived from complex-valued scalar filter banks;multiwavelet-transform-based image compression techniques;construction of two-dimensional multiwavelets on a triangulation;determination of the scaling parameters of affine fractal interpolation functions with the aid of wavelet analysis;scaled Gabor representation: a refined time-frequency decomposition;Riesz frames and finite-dimensional approaches to problems in frame theory;integration-free projection of a sampled signal on a multiresolution analysis ladder space via approximation theory;and high-accuracy reconstruction from wavelet coefficients.
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