Visibility of optical coherence tomography (OCT) images can be severely degraded by speckle noise. A computationally efficient despeckling approach that strongly reduces the speckle noise is reported. It is based on discrete wavelet transform (DWT), but eliminates the conventional process of threshold estimation. By decomposing an image into different levels, a set of sub-band images are generated, where speckle noise is additive. These sub-band images can be compounded to suppress the additive speckle noise, as DWT coefficients resulting from speckle noise tend to be approximately decorrelated. The final despeckled image is reconstructed by taking the inverse wavelet transform of the new compounded sub-band images. The performance of speckle reduction and edge preservation is controlled by a single parameter: the level of wavelet decomposition. The proposed technique is applied to intravascular OCT imaging of porcine carotid arterial wall and ophthalmic OCT images. Results demonstrate the effectiveness of this technique for speckle noise reduction and simultaneous edge preservation. The presented method is fast and easy to implement and to improve the quality of OCT images.
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http://dx.doi.org/10.1117/1.JBO.18.9.096002 | DOI Listing |
Coherent lensless imaging usually suffers from coherent noise and twin-image artifacts. In the terahertz (THz) range, where wavelengths are 2 to 4 orders of magnitude longer than those in the visible spectrum, the coherent noise manifests primarily as parasitic interference fringes and edge diffraction, rather than speckle noise. In this work, to suppress the Fabry-Pérot (F-P) interference fringes, we propose a novel method, which involves the averaging over multiple diffraction patterns that are acquired at equal intervals within a sample's half-wavelength axial shift.
View Article and Find Full Text PDFDeep learning has been widely used in phase unwrapping. However, owing to the noise of the wrapped phase, errors in wrap count prediction and phase calculation can occur, making it challenging to achieve high measurement accuracy under high-noise conditions. To address this issue, a three-stage multi-task phase unwrapping method was proposed.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Satellite Application Division, Korea Aerospace Research Institute (KARI), Daejeon 34133, Republic of Korea.
For change detection in synthetic aperture radar (SAR) imagery, amplitude change detection (ACD) and coherent change detection (CCD) are widely employed. However, time-series SAR data often contain noise and variability introduced by system and environmental factors, requiring mitigation. Additionally, the stability of SAR signals is preserved when calibration accounts for temporal and environmental variations.
View Article and Find Full Text PDFNeural Netw
January 2025
Hefei University of Technology, Hefei, 230601, China; The Key Laboratory of Knowledge Engineering with Big Data, Ministry of Education, Hefei, 230601, China.
Low-light image enhancement (LLIE) aims to improve the visibility and illumination of low-light images. However, real-world low-light images are usually accompanied with flares caused by light sources, which make it difficult to discern the content of dark images. In this case, current LLIE and nighttime flare removal methods face challenges in handling these flared low-light images effectively: (1) Flares in dark images will disturb the content of images and cause uneven lighting, potentially resulting in overexposure or chromatic aberration; (2) the slight noise in low-light images may be amplified during the process of enhancement, leading to speckle noise and blur in the enhanced images; (3) the nighttime flare removal methods usually ignore the detailed information in dark regions, which may cause inaccurate representation.
View Article and Find Full Text PDFUltrasound Med Biol
January 2025
Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong; Biomedical Engineering Programme, The University of Hong Kong, Hong Kong. Electronic address:
Objective: Near-field (NF) clutter filters are critical for unveiling true myocardial structure and dynamics. Randomized singular value decomposition (rSVD) stands out for its proven computational efficiency and robustness. This study investigates the effect of rSVD-based NF clutter filtering on myocardial motion estimation.
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