Multi-sensor Image Fusion Algorithm Research Articles

Neural network-based multisensor image fusion algorithm

Aiming at the problems of high mean square error and low fusion efficiency of existing fusion algorithms, a neural network-based multi-sensor image fusion algorithm is proposed. The fusion algorithm based on depth-separable convolution neural network (CNN) is determined by analyzing the quality evaluation and fusion methods of multi-sensor images, and summarizing the fusion rules. It is found that the integrity of image information acquisition is 97%, the mean square error is 4, and the fusion time is 2 s. Therefore, the algorithm has a good image fusion effect.

Optimization of the Oil Drilling Monitoring System Based on the Multisensor Image Fusion Algorithm

The working environment of the oil drilling platform is harsh, with many uncertain factors and high operating risks. During the drilling process, due to sudden formation factors or improper process operations, it is extremely easy to cause well wall instability, sticking, lost circulation, well kick, and blowout. In addition, other complicated situations and accidents have brought major challenges to drilling safety. In order to improve the technical level of oil and gas exploration and development and achieve the goal of reducing costs and increasing efficiency, it is necessary to strengthen the optimization of traditional oil drilling monitoring systems. This article summarizes the advantages and disadvantages of the existing image multiscale analysis algorithms, from wavelet transform, stationary wavelet transforms to contourlet transform, and nondownsampled contours based on the characteristics of the images collected by different sensors in the oil drilling monitoring system and the needs of practical applications. Wave transforms detailed comparison of the fusion performance of these image analysis algorithms under the same fusion rules. Aiming at the shortcoming of the large amount of calculation of nonsubsampled contourlet transform, a fast implementation algorithm (IFNSCT) is proposed. The multichannel filter bank structure is used to replace the original tree filter bank structure, which reduces the time‐consuming to the original without affecting the analysis performance of the algorithm. One‐half of the oil drilling monitoring efficiency has been improved.

Multi-sensor Image Fusion Algorithm Based on Multiresolution Analysis

To solve the fusion problem of visible and infrared images, based on image fusion algorithm such as region fusion, wavelet transform, spatial frequency, Laplasse Pyramid and principal component analysis, the quality evaluation index of image fusion was defined. Then, curve-let transform was used to replace the wavelet change to express the superiority of the curve. It integrated the intensity channel and the infrared image, and then transformed it to the original space to get the fused color image. Finally, two groups of images at different time intervals were used to carry out experiments, and the images obtained after fusion were compared with the images obtained by the first five algorithms, and the quality was evaluated. The experiment showed that the image fusion algorithm based on curve-let transform had good performance, and it can well integrate the information of visible and infrared images. It is concluded that the image fusion algorithm based on curve-let change is a feasible multi-sensor image fusion algorithm based on multi-resolution analysis.

Oil exploration oriented multi-sensor image fusion algorithm

Abstract In order to accurately forecast the fracture and fracture dominance direction in oil exploration, in this paper, we propose a novel multi-sensor image fusion algorithm. The main innovations of this paper lie in that we introduce Dual-tree complex wavelet transform (DTCWT) in data fusion and divide an image to several regions before image fusion. DTCWT refers to a new type of wavelet transform, and it is designed to solve the problem of signal decomposition and reconstruction based on two parallel transforms of real wavelet. We utilize DTCWT to segment the features of the input images and generate a region map, and then exploit normalized Shannon entropy of a region to design the priority function. To test the effectiveness of our proposed multi-sensor image fusion algorithm, four standard pairs of images are used to construct the dataset. Experimental results demonstrate that the proposed algorithm can achieve high accuracy in multi-sensor image fusion, especially for images of oil exploration.

A Multi Sensor Image Fusion Algorithm Based on Multi Resolution Analysis

A Multi Sensor Image Fusion Algorithm Based on Multi Resolution Analysis

Hybrid DDCT-PCA based multi sensor image fusion

Multi sensor image fusion algorithm based on directional Discrete Cosine Transform (DDCT) - Principal Component Analysis (PCA) hybrid technique has been developed and evaluated. The input images were divided into non-overlapping square blocks and the fusion process was carried out on the corresponding blocks. The algorithm works in two stages. In first stage, modes 0 to 8 were performed on images to be fused. For each mode, the coefficients from the images to be fused are used in the fusion process. The same procedure is repeated for other modes. Three different fusion rules are used in fusion process viz., 1. Averaging the corresponding coefficients (DDCTav), 2. Choosing the corresponding frequency band with maximum energy (DDCTek) and 3. Choosing the corresponding coefficient with maximum absolute value (DDCTmx) between the images. After this stage, there are eight fused images, one from each mode. In second stage, these eight fused images are fused using PCA. Performance of these algorithms were compared using fusion quality evaluation metrics such as root mean square error (RMSE), quality index (QI), spatial frequency and fusion quality index (FQI). It was concluded from the results that DDCTav performs poor and DDCTek performs slightly better than DDCTmx. Moreover, DDCTek is computationally simple and easily implementable on target hardware. Matlab code has been provided for better understanding.

Contrast-Enhanced Fusion of Multisensor Images Using Subband-Decomposed Multiscale Retinex

In this paper, we propose a novel pixel-level multi-sensor image fusion algorithm with simultaneous contrast enhancement. In order to accomplish both image fusion and contrast enhancement simultaneously, we suggest a modified framework of the subband-decomposed multiscale retinex (SDMSR), our previous contrast enhancement algorithm. This framework is based on a fusion strategy that reflects the multiscale characteristics of the SDMSR well. We first apply two complementary intensity transfer functions to source images in order to effectively utilize hidden information in both shadows and highlights in the fusion process. We then decompose retinex outputs into nearly nonoverlapping spectral subbands. The decomposed retinex outputs are then fused subband-by-subband, by using global weighting as well as local weighting to overcome the limitations of the pixel-based fusion approach. After the fusion process, we apply a space-varying subband gain to each fused subband-decomposed retinex output according to the subband characteristic so that the contrast of the fused image can be effectively enhanced. In addition, in order to effectively manage artifacts and noise, we make the degree of enhancement of fused details adjustable by improving a detail adjustment function. From experiments with various multi-sensor image pairs, the results clearly demonstrate that even if source images have poor contrast, the proposed algorithm makes it possible to generate a fused image with highly enhanced contrast while preserving visually salient information contained in the source images.

Landsat ETM+ and SAR image fusion based on generalized intensity Modulation

This work presents a novel multisensor image fusion algorithm, which extends panchrmomatic sharpening of multispectral (MS) data through intensity modulation to the integration of MS and synthetic aperture radar (SAR) imagery. The method relies on SAR texture, extracted by ratioing the despeckled SAR image to its low-pass approximation. SAR texture is used to modulate the generalized intensity (GI) of the MS image, which is given by a linear transform extending intensity-hue-saturation transform to an arbitrary number of bands. Before modulation, the GI is enhanced by injection of high-pass details extracted from the available panchrmomatic image by means of the a/spl grave/-trous wavelet decomposition. The texture-modulated panchrmomatic-sharpened GI replaces the GI calculated from the resampled original MS data. Then, the inverse transform is applied to obtain the fusion product. Experimental results are presented on Landsat-7 Enhanced Thematic Mapper Plus and European Remote Sensing 2 satellite images of an urban area. The results demonstrate accurate spectral preservation on vegetated regions, bare soil, and also on textured areas (buildings and road network) where SAR texture information enhances the fusion product, which can be usefully applied for both visual analysis and classification purposes.