127results about How to "Preserve image details" patented technology

The method comprises: reading out a digital image; saving the color and gray values of each pixel into an allocated memory; dividing the inputted image into a light illumination image and a reflection image; respectively making the treatment for both images; after treatment, said two images are combined into one image that is outputted to the output device. By the invention, the digital photos can get a high dynamical range (from the most brightness to the most darkness) according to the partial image information obtained at different light condition.

The invention discloses a stripe noise removal method for an infrared imaging system. The method includes the following implementation steps that first, a black body is imaged through the infrared imaging system, and an obtained infrared image is converted into a one-dimensional number sequence; second, FFT is performed on the obtained one-dimensional number sequence; third, a spectral line representing stripe noise in a spectrogram after transformation is calibrated; fourth, the result of the image which is also converted into the one-dimensional number sequence, is about to be denoised and has been subjected to FFT is processed at the position of the calibrated spectral line; fifth, inverse transformation is performed on the processed result so that the one-dimensional number sequence can be obtained, and then the two-dimensional image is formed through splicing. The method overcomes the defects of a traditional infrared image noise removal method, and is more effective and more accurate. Through the method, stripe noise in the infrared image can be effectively removed, loss of original information of the image is avoided, and the high-quality restoration image is obtained.

The invention provides a palm print database search method based on quantum algorithms. Four different quantum algorithms are respectively used in the three most important stages of palm print recognition. The search method comprises the following steps that 1, a palm print image is filtered by a quantum adaptive median filtering algorithm, and a fact can be observed from a filtering effect contrast chart that a better filtering effect can be obtained by the quantum adaptive median filtering algorithm utilized in the invention than a classic algorithm; 2, the characters of the palm print image are extracted by quantum Fourier conversion based on quantum parallelism, all palm print characters can be extracted by only one quantum Fourier conversion operation, and an analysis result shows that the extraction speed of the palm print characters is raised by the algorithm under the condition of well reserving palm print details; and 3, the palm print image is matched by a quantum set operation and a Grover algorithm. In comparison with the classic algorithm which requires N times of operation, the matching algorithm provided by the invention has the advantage that the position of the target palm print image can be found by only 1 / 2 N times of operation, and the probability of finding the position of the target image in the palm print database by the algorithm is close to 1.

The invention provides an image noise reduction method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a plurality of frames of original images, synthesizing according to the plurality of frames of original images to obtain a high-dynamic-range image, determining a matched noise reduction model according to ambient brightness, and performing noise reduction on the high-dynamic-range image by adopting the noise reduction model to obtain a target noise reduction image. According to the method, the ambient brightness of a plurality of frames of original images is collected, after a matched noise reduction model is determined, noise reduction processing is carried out on a high-dynamic-range image, and more image details are reserved while the image purity is ensured. In addition, noise reduction processing is carried out after multiple frames of original images are synthesized; therefore, both the bright light area and the dark light area in the synthesized high-dynamic-range image can be denoised, the noise reduction effectiveness is improved, image details are reserved while the image noise of the target noise-reduced image obtained through noise reduction is reduced, and an imaging effect with better definition is obtained.

The invention discloses a defective pixel detection and correction method and device, a storage medium and a terminal, and the method comprises the steps: moving a sliding window pixel by pixel in a to-be-detected image, and calculating the detail density corresponding to a central pixel through the pixel value of each pixel in the sliding window; after the sliding window is moved, determining a defective pixel threshold coefficient corresponding to the central pixel at least according to the detail density corresponding to the central pixel; after the sliding window is moved, dividing the sliding window into a plurality of sub-windows, calculating a defective pixel threshold range corresponding to each sub-window according to the pixel value in each sub-window and the defective pixel threshold coefficient corresponding to the central pixel, and comparing whether the pixel value of the central pixel falls into the defective pixel threshold range corresponding to each sub-window or not;and after the sliding window is moved, determining whether the central pixel is a defective pixel or not according to a comparison result of the central pixel and the defective pixel threshold rangecorresponding to each sub-window until the to-be-detected image is traversed. According to the technical scheme, the accuracy of defective pixel detection can be realized.

The invention belongs to the technical field of image processing, and particularly relates to a low-dose CT image denoising recursion algorithm based on a residual encoding and decoding network. According to the specific technical scheme, the invention discloses a shallow residual error coding and decoding recursive network for low-dose CT image denoising. The recursive shallow residual encoding and decoding network reduces the complexity of the network by reducing the number of layers and the number of convolution kernels in the residual encoding and decoding network. The network performanceis improved by using a recursion process. According to the algorithm, end-to-end mapping is trained through a network to obtain a high-quality image. At each recursion, original low-dose CT images arecascaded to the next input. The problem of distortion of the image after multiple recursions can be effectively avoided, image features can be better extracted, detail information of the image can bereserved, the complexity of the network can be reduced, the network performance can be improved, image details of the denoised image can be well reserved, and the image structure is clearer.

An image processing device for reducing image noise and the method thereof are provided. The method for reducing image noise includes the following steps. A filtering operation is performed on a plurality of pixel values of a first image according to a high-pass (HP) filtering mask, a first default filtering mask and a second default filtering mask to obtain a high-pass filtering image, a first low-pass (LP) image and a second LP image, respectively. A difference between the first and the second LP images is calculated to obtain a LP difference image. A pixel weighting calculation is performed according to the HP image and the LP difference image to obtain a pixel weighting mask corresponding to the first image. The first image is calculated according to the pixel weighting mask and one of the first and the second LP images to obtain a noise reducing image.