Multivariate Self-Dual Morphological Operators Based on Extremum Constraint

Marine monitoring systems have the requirements of a large field of view, low power consumption, real-time viewing, and economical and automatic functionality. This paper establishes an omnidirectional vision system used in marine buoys that meets these requirements. We present a framework for image stabilization, which is achieved by omnidirectional sea-skyline detection in a marine environment. We propose an optimal edge estimation method to calculate the sea-skyline ellipsis according to the sea-skyline characteristics in panoramic images. We construct a compact panoramic image stabilization model based on the sea-skyline and propose a reconstruction method for the invalid regions using the key frame. The experimental results and analysis show that the proposed approach is capable of acquiring stable video in real-time marine monitoring tasks and that the target detection is sufficiently effective, efficient, and accurate for a real-time ship target detection application.

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“…5. Perform edge thinning of the edge-detected image using morphological operators [30] for convenience in obtaining the edge length statistics.…”

Section: Sub-region Adaptive Canny Operatormentioning

confidence: 99%

Sea-skyline-based image stabilization of a buoy-mounted catadioptric omnidirectional vision system

Cai

Weng

Zhu

2018

J Image Video Proc.

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“…This reduces the effect of impulsive noise while preserving other image features. Another option is to use a non-linear filter, such as a morphological filter [6][7][8][9]. This smooths the edges of objects while preserving their shapes and is effective at removing impulsive noise.…”

Section: Introductionmentioning

confidence: 99%

New Real-Time High-Density Impulsive Noise Removal Method Applied to Medical Images

et al. 2023

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This paper introduces a new method for real-time high-density impulsive noise elimination applied to medical images. A double process aimed at the enhancement of local data composed of Nested Filtering followed by a Morphological Operation (NFMO) is proposed. The major problem with heavily noisy images is the lack of color information around corrupted pixels. We show that the classic replacement techniques all come up against this problem, resulting in average restoration quality. We only focus on the corrupt pixel replacement phase. For the detection itself, we use the Modified Laplacian Vector Median Filter (MLVMF). To perform pixel replacement, two-window nested filtering is suggested. All noise pixels in the neighborhood scanned by the first window are investigated using the second window. This investigation phase increases the amount of useful information within the first window. The remaining useful information that the second window failed to produce in the case of a very strong connex noise concentration is then estimated using a morphological operation of dilatation. To validate the proposed method, NFMO is first evaluated on the standard image Lena with a range of 10% to 90% impulsive noise. Using the Peak Signal-to-Noise Ratio metric (PSNR), the image denoising quality obtained is compared to the performance of a wide variety of existing approaches. Several noisy medical images are subjected to a second test. In this test, the computation time and image-restoring quality of NFMO are assessed using the PSNR and the Normalized Color Difference (NCD) criteria. Finally, an optimized design for a field-programmable gate array (FPGA) is suggested to implement the proposed method for real-time processing. The proposed solution performs excellent quality restoration for images with high-density impulsive noise. When the proposed NFMO is used on the standard Lena image with 90% impulsive noise, the PSNR reaches 29.99 dB. Under the same noise conditions, NFMO completely restores medical images in an average time of 23 milliseconds with an average PSNR of 31.62 dB and an average NCD of 0.10.

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“…Several approaches have been followed and validated. We can mainly cite the basic vector filters [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ], the weighted vector filters [ 8 , 9 ], the adaptive vector filters [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ], the hybrid vector filters [ 18 , 19 ], the fuzzy vector filters [ 20 , 21 , 22 ], the neural network based vector filters [ 23 , 24 , 25 , 26 ], and the morphological based vector median filters [ 27 , 28 ]. The choice of a method remains dependent on the application.…”

Section: Introductionmentioning

confidence: 99%

New Real-Time Impulse Noise Removal Method Applied to Chest X-ray Images

et al. 2022

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In this paper, we propose a new Modified Laplacian Vector Median Filter (MLVMF) for real-time denoising complex images corrupted by “salt and pepper” impulsive noise. The method consists of two rounds with three steps each: the first round starts with the identification of pixels that may be contaminated by noise using a Modified Laplacian Filter. Then, corrupted pixels pass a neighborhood-based validation test. Finally, the Vector Median Filter is used to replace noisy pixels. The MLVMF uses a 5 × 5 window to observe the intensity variations around each pixel of the image with a rotation step of π/8 while the classic Laplacian filters often use rotation steps of π/2 or π/4. We see better identification of noise-corrupted pixels thanks to this rotation step refinement. Despite this advantage, a high percentage of the impulsive noise may cause two or more corrupted pixels (with the same intensity) to collide, preventing the identification of noise-corrupted pixels. A second round is then necessary using a second set of filters, still based on the Laplacian operator, but allowing focusing only on the collision phenomenon. To validate our method, MLVMF is firstly tested on standard images, with a noise percentage varying from 3% to 30%. Obtained performances in terms of processing time, as well as image restoration quality through the PSNR (Peak Signal to Noise Ratio) and the NCD (Normalized Color Difference) metrics, are compared to the performances of VMF (Vector Median Filter), VMRHF (Vector Median-Rational Hybrid Filter), and MSMF (Modified Switching Median Filter). A second test is performed on several noisy chest x-ray images used in cardiovascular disease diagnosis as well as COVID-19 diagnosis. The proposed method shows a very good quality of restoration on this type of image, particularly when the percentage of noise is high. The MLVMF provides a high PSNR value of 5.5% and a low NCD value of 18.2%. Finally, an optimized Field-Programmable Gate Array (FPGA) design is proposed to implement the proposed method for real-time processing. The proposed hardware implementation allows an execution time equal to 9 ms per 256 × 256 color image.

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Multivariate Self-Dual Morphological Operators Based on Extremum Constraint

Cited by 3 publications

References 40 publications

Sea-skyline-based image stabilization of a buoy-mounted catadioptric omnidirectional vision system

Sea-skyline-based image stabilization of a buoy-mounted catadioptric omnidirectional vision system

New Real-Time High-Density Impulsive Noise Removal Method Applied to Medical Images

New Real-Time Impulse Noise Removal Method Applied to Chest X-ray Images

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