Iterative Fourier transform algorithms are widely used for creating holograms in holographic image projection. However, the reconstructed image always suffers from the speckle noise severely due to the uncontrolled phase distribution of the image. In this paper, a new iterative method is proposed to eliminate the speckle noise. In the iteration, the amplitude and phase in the signal window in the output plane are constrained to the desired distribution and a special object-dependent quadratic phase distribution, respectively. Since the phase of the reconstructed image is assigned artificially, the speckle noise came from the destructive interference between the sampling points with random and erratic phase distribution can be eliminated. To verify the method, simulations and experiments are performed. And the result shows that high quality, low noise images can be achieved.
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http://dx.doi.org/10.1364/OE.24.022766 | DOI Listing |
Neural Netw
January 2025
School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China.
Synthetic aperture radar (SAR) images are crucial in remote sensing due to their ability to capture high-quality images regardless of environmental conditions. Though it has been studied for years, the following aspects still limit its further improvement. (1) Due to the unique imaging mechanism of SAR images, the influence of speckle noise cannot be avoided.
View Article and Find Full Text PDFCoherent 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.
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