scholarly journals Real Time Depth Hole Filling using Kinect Sensor and Depth Extract from Stereo Images

2019 ◽  
Vol 12 (Issue 3) ◽  
pp. 115-122
Author(s):  
Kapil Raviya ◽  
Ved Vyas Dwivedi ◽  
Ashish Kothari ◽  
Gunvantsinh Gohil
Keyword(s):  
Real Time ◽  
Stereo Matching ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Depth Map ◽  
Obstacle Detection ◽  
Block Matching ◽  
Microsoft Kinect ◽  
Hole Filling ◽  
Mathematical Algorithm

The researcher have suggested real time depth based on frequency domain hole filling. It get better quality of depth sequence generated by sensor. This method is capable to produce high feature depth video which can be quite useful in improving the performance of various applications of Microsoft Kinect such as obstacle detection and avoidance, facial tracking, gesture recognition, pose estimation and skeletal. For stereo matching approach images depth extraction is the hybrid (Combination of Morphological Operation) mathematical algorithm. There are few step like color conversion, block matching, guided filtering, minimum disparity assignment design, mathematical perimeter, zero depth assignment, combination of hole filling and permutation of morphological operator and last nonlinear spatial filtering. Our algorithm is produce smooth, reliable, noise less and efficient depth map. The evaluation parameter such as Structure Similarity Index Map (SSIM), Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) measure the results for proportional analysis.

scholarly journals Image Denoising in Spatial and Transform Domains

2021 ◽  
Author(s):  
Mina Sharifymoghaddam
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Weighting Scheme ◽  
Block Matching ◽  
Hard Thresholding ◽  
Weighted Version ◽  
Nonlocal Means ◽  
Processing Step ◽  
Index Measure

Image denoising is an inseparable pre-processing step of many image processing algorithms. Two mostly used image denoising algorithms are Nonlocal Means (NLM) and Block Matching and 3D Transform Domain Collaborative Filtering (BM3D). While BM3D outperforms NLM on variety of natural images, NLM is usually preferred when the algorithm complexity is an issue. In this thesis, we suggest modified version of these two methods that improve the performance of the original approaches. The conventional NLM uses weighted version of all patches in a search neighbourhood to denoise the center patch. However, it can include some dissimilar patches. Our first contribution, denoted by Similarity Validation Based Nonlocal Means (NLM-SVB), eliminates some of those unnecessary dissimilar patches in order to improve the performance of the algorithm. We propose a hard thresholding pre-processing step based on the exact distribution of distances of similar patches. Consequently, our method eliminates about 60% of dissimilar patches and improves NLM in terms of Peak Signal to Noise Ratio (PSNR) and Stracuteral Similarity Index Measure (SSIM). Our second contribution, denoted by Probabilistic Weighting BM3D (PW-BM3D), is the result of our thorough study of BM3D. BM3D consists of two main steps. One is finding a basic estimate of the noiseless image by hard thresholding coefficients. The second one is using this estimate to perform wiener filtering. In both steps the weighting scheme in the aggregation process plays an important role. The current weighting process depends on the variance of retrieved coefficients after denoising which results in a biased weighting. In PW-BM3D, we propose a novel probabilistic weighting scheme which is a function of the probability of similarity of noiseless patches in each 3D group. The results show improvement over BM3D in terms of PSNR for an average of about 0.2dB.

scholarly journals Image Denoising in Spatial and Transform Domains

2021 ◽  
Author(s):  
Mina Sharifymoghaddam
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Weighting Scheme ◽  
Block Matching ◽  
Hard Thresholding ◽  
Weighted Version ◽  
Nonlocal Means ◽  
Processing Step ◽  
Index Measure

Image denoising is an inseparable pre-processing step of many image processing algorithms. Two mostly used image denoising algorithms are Nonlocal Means (NLM) and Block Matching and 3D Transform Domain Collaborative Filtering (BM3D). While BM3D outperforms NLM on variety of natural images, NLM is usually preferred when the algorithm complexity is an issue. In this thesis, we suggest modified version of these two methods that improve the performance of the original approaches. The conventional NLM uses weighted version of all patches in a search neighbourhood to denoise the center patch. However, it can include some dissimilar patches. Our first contribution, denoted by Similarity Validation Based Nonlocal Means (NLM-SVB), eliminates some of those unnecessary dissimilar patches in order to improve the performance of the algorithm. We propose a hard thresholding pre-processing step based on the exact distribution of distances of similar patches. Consequently, our method eliminates about 60% of dissimilar patches and improves NLM in terms of Peak Signal to Noise Ratio (PSNR) and Stracuteral Similarity Index Measure (SSIM). Our second contribution, denoted by Probabilistic Weighting BM3D (PW-BM3D), is the result of our thorough study of BM3D. BM3D consists of two main steps. One is finding a basic estimate of the noiseless image by hard thresholding coefficients. The second one is using this estimate to perform wiener filtering. In both steps the weighting scheme in the aggregation process plays an important role. The current weighting process depends on the variance of retrieved coefficients after denoising which results in a biased weighting. In PW-BM3D, we propose a novel probabilistic weighting scheme which is a function of the probability of similarity of noiseless patches in each 3D group. The results show improvement over BM3D in terms of PSNR for an average of about 0.2dB.

scholarly journals Real-time RGB-D image stitching using multiple Kinects for improved field of view

2017 ◽  
Vol 14 (2) ◽  
pp. 172988141769556 ◽  
Author(s):  
Hengyu Li ◽  
Hang Liu ◽  
Ning Cao ◽  
Yan Peng ◽  
Shaorong Xie ◽  
...  
Keyword(s):  
Real Time ◽  
Anisotropic Diffusion ◽  
Color Image ◽  
Depth Map ◽  
Color Images ◽  
Field Of View ◽  
Hole Filling ◽  
Depth Data ◽  
Registration Data ◽  
Filling Method

This article concerns the problems of a defective depth map and limited field of view of Kinect-style RGB-D sensors. An anisotropic diffusion based hole-filling method is proposed to recover invalid depth data in the depth map. The field of view of the Kinect-style RGB-D sensor is extended by stitching depth and color images from several RGB-D sensors. By aligning the depth map with the color image, the registration data calculated by registering color images can be used to stitch depth and color images into a depth and color panoramic image concurrently in real time. Experiments show that the proposed stitching method can generate a RGB-D panorama with no invalid depth data and little distortion in real time and can be extended to incorporate more RGB-D sensors to construct even a 360° field of view panoramic RGB-D image.

scholarly journals COMPARISON OF IMAGE ENHANCEMENT METHODS FOR CHROMOSOME KARYOTYPE IMAGE ENHANCEMENT

2017 ◽  
Vol 10 (1) ◽  
pp. 50 ◽  
Author(s):  
Dewa Made Sri Arsa ◽  
Grafika Jati ◽  
Agung Santoso ◽  
Rafli Filano ◽  
Nurul Hanifah ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Histogram Equalization ◽  
Block Matching ◽  
Structural Similarity Index ◽  
Method Performance ◽  
Enhancement Method ◽  
Basic Image

The chromosome is a set of DNA structure that carry information about our life. The information can be obtained through Karyotyping. The process requires a clear image so the chromosome can be evaluate well. Preprocessing have to be done on chromosome images that is image enhancement. The process starts with image background removing. The image will be cleaned background color. The next step is image enhancement. This paper compares several methods for image enhancement. We evaluate some method in image enhancement like Histogram Equalization (HE), Contrast-limiting Adaptive Histogram Equalization (CLAHE), Histogram Equalization with 3D Block Matching (HE+BM3D), and basic image enhancement, unsharp masking. We examine and discuss the best method for enhancing chromosome image. Therefore, to evaluate the methods, the original image was manipulated by the addition of some noise and blur. Peak Signal-to-noise Ratio (PSNR) and Structural Similarity Index (SSIM) are used to examine method performance. The output of enhancement method will be compared with result of Professional software for karyotyping analysis named Ikaros MetasystemT M . Based on experimental results, HE+BM3D method gets a stable result on both scenario noised and blur image. 

scholarly journals REAL-TIME DEPTH MAP ESTIMATION FROM INFRARED STEREO IMAGES OF RGB-D CAMERAS

2021 ◽  
Vol V-2-2021 ◽  
pp. 107-112
Author(s):  
J. Zhong ◽  
M. Li ◽  
X. Liao ◽  
J. Qin ◽  
H. Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Block Size ◽  
Depth Map ◽  
Measurement Range ◽  
Block Matching ◽  
Depth Information ◽  
Depth Maps ◽  
Sensing Systems ◽  
Global Matching ◽  
Efficient Matching

Abstract. RGB-D cameras are novel sensing systems that can rapidly provide accurate depth information for 3D perception, among which the type based on active stereo vision has been widely used. However, there are some problems exiting in use, such as the short measurement range and incomplete depth maps. This paper presents a robust and efficient matching algorithm based on semi-global matching to obtain more complete and accurate depth maps in real time. Considering characteristics of captured infrared speckle images, the Gaussian filter is performed firstly to restrain noise and enhance the relativity. It also adopts the idea of block matching for reliability, and a dynamic threshold selection of the block size is used to adapt to various situation. Moreover, several optimizations are applied to improve precision and reduce error. Through experiments on the Intel Realsense R200, the excellent capability of our proposed method is verified.

Stereo Reconstruction of Infrared Images Captured in Microwave Reactor

2013 ◽  
Vol 846-847 ◽  
pp. 1007-1010
Author(s):  
Yin Hui Zhang ◽  
Jin Hui Peng ◽  
Zi Fen He
Keyword(s):  
Temperature Distribution ◽  
Real Time ◽  
Vision System ◽  
Image Data ◽  
Depth Map ◽  
Reaction Chamber ◽  
Block Matching ◽  
Heating Process ◽  
Microwave Reactor ◽  
Stereo Reconstruction

During metallurgy and food processing it is always necessary for real-time detection of stereo temperature distribution in reaction cavity of microwave. We focus on the stereo reconstruction of temperature distribution of the microwave reaction chamber using real-time image data captured by infrared sensor during heating process. Due to the stereo depth of the scene in humans vision system is mapped through disparity, thus the key technology stereo reconsturction of temperature is to find the corresponding points in the image pair. We employ the block matching algorithm to search the correspondence by minimizing a cost function derived on a hierarchical tree structured pyramid probabilistic graph. The study result shows that in the back projection of the depth map, the walls, ceiling, and floor of the microwave reactor all appear mutually orthogonal, which illustrates that the stereo temperature field is well reconstructed.

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