Adaptive Window Matching Research Articles

Improvement of AD-Census Algorithm Based on Stereo Vision.

Problems such as low light, similar background colors, and noisy image acquisition often occur when collecting images of lunar surface obstacles. Given these problems, this study focuses on the AD-Census algorithm. In the original Census algorithm, in the bit string calculated with the central pixel point, the bit string will be affected by the noise that the central point is subjected to. The effect of noise results in errors and mismatching. We introduce an improved algorithm to calculate the average window pixel for solving the problem of being susceptible to the central pixel value and improve the accuracy of the algorithm. Experiments have proven that the object contour in the grayscale map of disparity obtained by the improved algorithm is more apparent, and the edge part of the image is significantly improved, which is more in line with the real scene. In addition, because the traditional Census algorithm matches the window size in a fixed rectangle, it is difficult to obtain a suitable window in the image range of different textures, affecting the timeliness of the algorithm. An improvement idea of area growth adaptive window matching is proposed. The improved Census algorithm is applied to the AD-Census algorithm. The results show that the improved AD-Census algorithm has been shown to have an average run time of 5.3% and better matching compared to the traditional AD-Census algorithm for all tested image sets. Finally, the improved algorithm is applied to the simulation environment, and the experimental results show that the obstacles in the image can be effectively detected. The improved algorithm has important practical application value and is important to improve the feasibility and reliability of obstacle detection in lunar exploration projects.

Depth Estimation for Lytro Images by Adaptive Window Matching on EPI

A depth estimation algorithm from plenoptic images is presented. There are two stages to estimate the depth. First is the initial estimation base on the epipolar plane images (EPIs). Second is the refinement of the estimations. At the initial estimation, adaptive window matching is used to improve the robustness. The size of the matching window is based on the texture description of the sample patch. Based on the texture entropy, a smaller window is used for a fine texture. A smooth texture requires a larger window. With the adaptive window size, different reference patches based on various depth are constructed. Then the depth estimation compares the similarity among those patches to find the best matching patch. To improve the initial estimation, a refinement algorithm based on the Markov Random Field (MRF) optimization is used. An energy function keeps the data similar to the original estimation, and then the data are smoothed by minimizing the second derivative. Depth values should satisfy consistency across multiple views.

Hyperspectral Remote Sensing Data Recovery via Adaptive Window Matching Method

HJ-1A satellite is often used to monitor environmental disaster and plays an important role in environmental changes. Because of the affection of various factors, certain band of HJ-1A hyperspectral remote sensing data is severe loss or distortion, which brings great difficulties for subsequent quantitative processing. A novel adaptive window matching algorithm, which can adjust intelligently size of matching window according to different local feature information of the image, is proposed for HJ-1A satellite hyperspectral data recovery in this paper. The results show that the adaptive matching algorithm has a more superior performance than other algorithms in image quality index, column mean curve, and image correlation coefficient.

Regularized Stereo Matching Scheme using Adaptive Disparity Estimation

In this paper, a regularized stereo matching scheme using adaptive disparity estimation is proposed. That is, by adaptively predicting the mutual correlation between stereo image pair using the proposed algorithm, the bandwidth of an input stereo image pair can be compressed to the level of a conventional two-dimensional (2D) image and a predicted image can also be effectively reconstructed using a reference image and disparity vectors. In the adaptive disparity estimation method, feature values are extracted from an input stereo image pair and a matching window for stereo matching is adaptively selected depending on the magnitude of the feature values. Accordingly, this adaptive window matching algorithm (AMA) could improve the overall performance of stereo matching by reducing the mismatch of disparity vectors, which occurs in the conventional dense disparity estimation with a small matching window, and also by reducing a blocking effect, which occurs in the coarse disparity estimation with a large matching window. However, it still has problems of overlap and disallocation of matching windows. Therefore, to alleviate those problems, a new regularized adaptive disparity estimation method is proposed in this paper. That is, the estimated disparity vector is regularized with the neighboring disparity vectors; as a result, the predicted stereo image is found to be more effectively reconstructed. Some experiments with stereo sequences of “Man”, “Hoon”, “Yong”, and “Car” reveal that the proposed algorithm improves the peak signal-to-noise ratios (PSNRs) of the reconstructed images by 8.47 and 1.53 dB, on average, compared with those in the cases of feature and pixel-based window matching algorithm (FPMA) and AMA, respectively.

Effective reconstruction of stereoscopic image pair by using regularized adaptive window matching algorithm

In this paper, an effective method for reconstruction of stereoscopic image pair through the regularized adaptive disparity estimation is proposed. Although the conventional adaptive disparity window matching can sharply improve the PSNR of a reconstructed stereo image, but there still exist some problems of overlapping between the matching windows and disallocation of the matching windows, because the size of the matching window tend to changes adaptively in accordance with the magnitude of the feature values. In the proposed method, the problems relating to the conventional adaptive disparity estimation scheme can be solved and the predicted stereo image can be more effectively reconstructed by regularizing the extimated disparity vector with the neighboring disparity vectors. From the experimental results, it is found that the proposed algorithm show improvements the PSNR of the reconstructed right image by about 2.36 ∼ 2.76 dB, on average, compared with that of conventional algorithms.