37results about How to "Improve super-resolution" patented technology

The method uses multi low resolution and low dynamic imagers to incorporates the sub-pixel dynamical image formation technology with the technology of multi image reconstructing high dynamical image, and uses a special site distribution of image sensor to implement the reconstructing of ultra-high resolution image, and uses image gray level interpolation to reconstruct image gray level so as to get a high dynamic range and high resolution image.

The pupil filter far-field super-resolution imaging system and pupil filter design method belong to the field of super-resolution imaging technology, in order to solve the problem that the final super-resolution imaging is seriously affected in the existing field diaphragm scanning far-field super-resolution imaging system For quality issues, the system is set up in sequence along the light incident direction: front-end optical objective lens, field diaphragm, collimator lens group, pupil filter, imaging lens and CCD detector, and the front-end optical objective lens is used to image distant scenes At the intermediate image plane of the system, the field diaphragm is placed on the rear focal plane of the front optical objective lens, that is, at the intermediate image plane of the entire system, the front focus of the collimator group is at the intermediate image plane of the system, and the pupil filter is placed on the front optical The exit pupil position of the rear end of the system combined with the objective lens and the collimating lens group, and the position and size of the effective aperture of the pupil filter coincide with the exit pupil surface, the imaging lens will perform secondary imaging on the light passing through the pupil filter, and the CCD detector The target plane coincides with the secondary imaging plane.

Provided is a lung 4D-CT image super-resolution reconstruction method based on registration. The lung 4D-CT image super-resolution reconstruction method based on registration sequentially comprises the steps that (1) a sequence of low-resolution images with different phases is obtained through lung 4D-CT data; (2) the image, with some phase, in the sequence is selected as a reference image, interpolation amplification is carried out on the image, and the result obtained after interpolation serves as an initial estimated image f<0> of a reconstruction result; (3) the corresponding low-resolution images, with other phases, in the sequence serve as floating images, interpolation amplification is carried out on the floating images, motion deformation fields between the interpolation results of the floating images and the initial estimated image f<0> are estimated respectively; (4) a high-resolution lung 4D-CT image is reconstructed on the basis of the motion deformation fields obtained in the step (3). A multi-plane display image of the lung 4D-CT image obtained through the lung 4D-CT image super-resolution reconstruction method based on registration is clear, the structure is obviously improved, the image resolution is improved, and the quality of the multi-plane display image of the lung 4D-CT data can be effectively improved.

The invention discloses application of a specific single domain antibody for a V5 tag protein. The single domain antibody has amino acid sequences as shown in SEQ ID NO:1, SEQ ID NO:9, SEQ ID NO:17, SEQ ID NO:25, SEQ ID NO:33, SEQ ID NO:41, SEQ ID NO:49, SEQ ID NO:57 and SEQ ID NO:65-72; by adopting gene sequences and host cells of the single domain antibody disclosed by the invention, the singledomain antibody can be efficiently expressed and applied to stochastic optical reconstruction microscopy (STORM) and photo-activated localization microscopy (PALM) to trace and detect target proteinsin a biological sample.

An image processing method, system, device and medium provided by the present invention, aiming at the current problems, designs an image processing method, which can distinguish high-definition images and low-definition images from real images; and also designs a The degradation model can first learn the characteristics of the low-definition image, and generate a simulated low-definition image corresponding to the high-definition image one by one, which solves the problem that the super-resolution model is difficult to train on the real image; at the same time, the present invention also designs a filter mechanism, Splitting the image into high-frequency and low-frequency parts significantly improves the super-resolution effect of real images. Moreover, the present invention has versatility, and according to different application scenarios, the optimal super-resolution effect of the scenario can be obtained only by using corresponding real images for training. Moreover, the image after the image quality is improved by the super-resolution model in the present invention has no distortion in color, clear details, sharp edges, and a good overall look and feel.

The invention discloses a V5-tag protein specifically targeted single-domain antibody and derived proteins thereof. The V5-tag protein specifically targeted single-domain antibody has the amino acid sequences shown as SEQ ID NO: 1, SEQ ID NO: 9, SEQ ID NO: 17, SEQ ID NO: 25, SEQ ID NO: 33, SEQ ID NO: 41, SEQ ID NO: 49, SEQ ID NO: 57 and SEQ ID NO: 65-72. Through the gene sequences and the host cells of the V5-tag protein specifically targeted single-domain antibody, the V5-tag protein specifically targeted single-domain antibody can be expressed efficiently and can be applied to stochastic optical reconstruction microscopy (STORM) and Photo-activated localization microscopy (PLAM) for tracing and detecting target proteins in biological samples.

The invention provides a method of using natural image prior-knowledge for multi-image super-resolution reconstruction. According to the method, a fields-of-experts model obtained by learning is introduced to be used as the natural image prior-knowledge in a process of using a series of low-resolution images for super-resolution reconstruction, and quality of multi-image super-resolution reconstruction is improved. Compared with prior knowledge of L1-norm, total variation and the like used by traditional Bayesian multi-image super-resolution methods, the natural image prior-knowledge used in the method is the fields-of-experts model obtained through training of an image database, and can better extract statistical features of natural scenes, and thus obtain better super-resolution reconstruction effects. The method simplifies partial processes of multi-image super-resolution, and shortens calculation time.

The invention discloses a method for super-resolution imaging of ships in a circular-scan ISAR mode, which belongs to the technical field of radar and comprises the following steps: dividing echo signals in the circular-scan ISAR mode into M data frames, and processing them frame by frame; In the mth data frame, the range compression is performed, and the sub-echo of the ship target in the m-th frame is selected; the sub-echo of the ship target in the m-th frame is preprocessed including translation compensation and rotation compensation; using Burg Algorithm, for the sub-echo of the ship target in the m-th frame, data extrapolation is performed in the azimuth direction; the extrapolated data is randomly sampled in the azimuth direction; for the sampled data, the compressed sensing calculation is performed in the azimuth direction to obtain The sparse coefficient matrix of the current data frame; the constant false alarm rate detection is performed on the sparse coefficient matrix after compressed sensing, and the range Doppler domain super-resolution image of the mth frame is obtained.