78results about How to "Improve fusion quality" patented technology

An adaptive and synergic fill welding method and apparatus enables automatic, or adaptive fill, and user directed, or synergic fill, modes to provide improved fusion quality, by ensuring that base metal dilution of a weld remains within a predetermined range. The apparatus includes a means for profiling and tracking a joint, and multi-part adjustable welding means. In adaptive fill mode the method automatically varies a plurality of welding parameters in response to measured variations such as joint width between work pieces. In synergic fill mode, the method enables a user to vary multiple welding parameters in response to joint variations by adjusting a single variable, a synergic fill number, which may be controlled by means of a user interface pendent. The multiple welding parameters may include predetermined wire feed speed, torch travel speed, welding voltage and current, torch oscillation width, dwell time, and a plurality of bead size parameters.

An apparatus for fusing a pipe to a fitting includes an induction heating element wholly encased or coated with a flusible thermoplastic polymeric material, a fitting having a socket or a flange. The induction heating element is adapted to be inserted into said socket or mounted on said flange, the fitting and the element together defining a throughbore, a portion of a pipe adapted to be inserted into the throughbore or mountable on said flange. The apparatus also includes a locating and maintaining member on the induction heating element with one or more projections adapted to be snap-fitted on the fitting, the pipe or both, and capable of locating and maintaining the position of the heating element relative to the fitting or the member portion. The induction fusion element is capable of fusing the fitting and the pipe portion.

The invention discloses a dual-light image fusion model based on a depth convolution antagonism generation network DCGAN. The model extracts image features of the same target under visible light and infrared light through a depth discrimination convolution network, and sparsely encodes the two image features according to the same feature dictionary; the dual-light image fusion model extracts imagefeatures of the same target under visible light and infrared light through a depth discrimination convolution network. Then the coding feature is fused and used as the input data of the depth convolution generation network, so that the generation network generates the fusion image. Finally, the error training model between the fusion feature and the coding fusion feature is used to generate the dual-light fusion image. The model uses depth learning network to extract and encode the features of visible and infrared images, and the feature points of the two images can be automatically matched by fusing the features of the encoding. The model of the invention can be called at any time after training, and the double-light image with high fusion quality can be automatically generated by inputting visible light image and infrared light image at the same time.

The present invention discloses a multi-mode medical image fusion method based on low-rank decomposition and sparse representation. The method includes the following steps that: two different multi-mode medical images to be fused are subjected to low-rank decomposition, so that a low-rank part image and a sparse part image are obtained respectively; a KSVD (K-means singular value decomposition) algorithm is adopted to train a selected non-medical image set so that a low-rank dictionary can be obtained, and the KSVD (K-means singular value decomposition) algorithm is utilized to perform low-rank decomposition on the selected non-medical image set, so that a sparse part image set can be obtained, and the sparse part image set is trained, so that a sparse dictionary can be obtained; a sparse representation method is adopted to sparsely reconstruct the low-rank part image and the sparse part image, so that a low-rank reconstructed image and a sparse reconstructed image can be obtained; the sparse representation method is adopted to sparsely fuse the low-rank reconstructed image and the sparse reconstructed image, so that a fused image can be obtained; the difference values of the two multi-mode medical images and the sparse reconstructed image and the low-rank reconstructed image are calculated; and the difference values are added into the fused image, so that a final sparse fused image can be obtained. The subjective and objective evaluation indexes of the multi-mode medical image fusion method of the invention are better than the indexes of a traditional fusion method.

The invention discloses an SAR image fusion method based on a multi-scale geometric analysis tool and belongs to the field of image processing technology; and the method mainly solves the problems that the prior fusion method has phenomenon of fuzzy and massive detailed compositions. The method is realized through the following steps: (1) two source images are subjected to image fusion respectively based on wavelet transformation to obtain a fusion result img_wave; (2) the two source images are subjected to image fusion based on Contourlet conversion to obtain a fusion result img_cont; (3) information entropy, average gradient and standard deviation of the two fusion results img_wave and img_cont are calculated respectively; (4) the information entropy, average gradient and standard deviation of the image fusion result img_wave based on wavelet transformation and the information entropy, average gradient and standard deviation of the image fusion result img_cont based on Contourlet conversion are compared so as to judge the quality of the fusion results; and (5) according to a judgment result, secondary fusion is selected. The SAR image fusion method improves the information content of the fused image, ensures the definition of the image and can be used for fusion of an SAR image, a natural image and a medical image.

The invention discloses a method for multi-focus image fusion based on compressed sensing, and belongs to the technical field of image signal processing. The method includes the steps: firstly, respectively sampling two images to be fused in a compressed manner to obtain observation vectors of the two images; secondly, fusing the observation vectors of the two images to obtain fusion observation vectors; and thirdly, reconstructing a fused image of the two images to be fused by the aid of the fusion observation vectors. The observation vectors are fused by the aid of ALEM (adaptive local energy measure) fusion rules, and the fused image is reconstructed by the aid of an FCLALM (fast continuous linear augmentation Lagrangian method). The invention further discloses a device for multi-focus image fusion based on compressed sensing. Compared with the prior art, the method and the device have the advantages that higher image fusion quality can be achieved, and signal-to-noise ratio and convergence speed are higher.

The invention relates to a new multi-focus image fusion method based on a convolutional neural network and dynamic guided filtering. By constructing a convolutional neural network for focus detection,direct mapping from a source image to a focus image is generated, manual operation is avoided, pixel distribution maps of a focusing area and a non-focusing area are obtained, and then a high-qualityfused image is obtained through dynamic guided filtering operation of small area removal and edge reservation.