11 results about "Value noise" patented technology

Apparatuses, systems, and techniques to generate blue noise masks for real-time image rendering and enhancement. In at least one embodiment, a vector-valued noise mask is generated and applied to one or more images to generate one or more enhanced images for image processing (e.g., real-time image rendering). In at least one embodiment, the noise mask includes vector values per pixel and is able to handle the temporal domain (e.g., add time to the spatial domain) to improve image quality when rendering images over multiple frames.

The application relates to a hand-drawn sketch synthesis method based on a vector diagram, a computer device and a program product. The hand-drawn sketch synthesis method comprises the following steps: obtaining a vector diagram based on a content image, and obtaining a hand-drawn sketch based on vector diagram rendering; superimposing random noise on the hand-drawn sketch to obtain a noisy image, and inputting the noisy image into a diffusion model; obtaining a corresponding text description based on the content image, performing edge detection on the content image to generate an edge diagram, injecting the features of both the text description and the edge diagram into the diffusion model, and guiding the diffusion model to generate a natural image; obtaining predicted noise in the process of generating the natural image by the diffusion model according to the natural image and the noisy image, and obtaining difference noise according to the predicted noise and the random noise; updating and optimizing the hand-drawn sketch by using the difference loss of the content image and the hand-drawn sketch; and updating and optimizing the hand-drawn sketch by using the difference noise. The application improves the content structure and semantic structure similarity of the hand-drawn sketch compared with the content image.

The present invention discloses an adaptive dual-recording video picture exposure assessment method based on histogram fusion, which relates to the technical field of dual-recording video quality detection. By dividing the local key areas of the video picture, the global brightness image and the local brightness image are uniformly scaled to obtain a normalized global brightness image and a normalized local brightness image, which is conducive to performing histogram probability distribution statistics on the same pixel scale, avoiding the noise disturbance of pixel data sample values ​​and the influence of the sampling number on the histogram. The weighted superposition of the global and local brightness histograms is combined, and a probability truncation operation is introduced to eliminate noise interference, and finally a comprehensive exposure quality score is calculated by fusing the overexposure and underexposure deviations. The present invention solves the limitations of relying solely on global or local brightness analysis, significantly improves the exposure quality inspection accuracy of key areas in dual-recording videos, and reduces the influence of noise on statistical distribution through truncation correction.

The present disclosure relates to spatio-temporal noise masks and sampling using vectors for image processing and light transport simulation systems and applications, and in particular to apparatuses, systems, and techniques for generating blue noise masks for real-time image rendering and augmentation. In at least one embodiment, a vector-valued noise mask is generated and applied to one or more images to generate one or more augmented images for image processing (e.g., real-time image rendering). In at least one embodiment, the noise mask includes a vector value per pixel and is capable of handling the temporal domain (e.g., adding time to the spatial domain) to improve image quality when rendering images over multiple frames.

The present invention discloses a multi-piece refined dense matching method guided by laser point cloud. The existing real-scene three-dimensional reconstruction process is prone to problems such as information loss and large noise fluctuations. The present invention includes obtaining the basic situation of the survey area, and designing a solution for collaboratively collecting oblique image data and laser point cloud data; collecting multi-source data to complete the coarse matching between heterogeneous data; through the precise matching of the coordinate system, making the final residual error of the heterogeneous perception data system below the sub-ground resolution; initializing the same-name points, anchoring the laser point cloud data in each image; depth map diffusion, depth map fusion, eliminating redundant observations; noise filtering and compensation, eliminating fluctuation points, and then back-projecting them into each depth map to fill the holes; and finally exporting the dense point cloud. The present invention solves the problems of unreliable and poor-quality data results generated by a single data source, as well as the superposition of redundant data and the submergence of high-precision geometric information.

The invention discloses a self-adaptive double-recording video picture exposure evaluation method based on histogram fusion, and relates to the technical field of double-recording video quality detection.A global brightness image and a local brightness image are uniformly zoomed by dividing a local key area of a video picture; the normalized global brightness image and the normalized local brightness image are obtained, histogram probability distribution statistics on the same pixel scale is facilitated, and the influence of pixel data sample value noise disturbance and the sampling number on the histogram is avoided. Weighted superposition of global and local brightness histograms is combined, probability truncation operation is introduced to eliminate noise interference, and finally a comprehensive exposure quality score is calculated by fusing overexposure and underexposure deviation degrees. According to the method, the limitation of single dependence on global or local brightness analysis is solved, the exposure quality inspection precision of the key area in the double-recording video is remarkably improved, and meanwhile, the influence of noise on statistical distribution is reduced through truncation correction.

A system and method of analog to digital conversion including an adjustable ADC, FIR filter circuitry, and a noise setting controller. The ADC samples an analog input signal to provide digital samples at a sample rate that is Y times an output rate of output digital values. The FIR filter circuitry includes Y taps with Y corresponding coefficients and is configured to filter the digital samples from the ADC and to provide filtered digital samples at the sample rate. decimation circuitry may be included to decimate the filtered digital samples by Y to provide the output digital values. The noise setting controller provides an adjustment value to the ADC to adjust noise contribution of the digital samples provided by the ADC based on corresponding coefficients of the FIR filter circuitry. The ADC is adjusted to reduce noise contribution of digital samples that correspond with higher FIR filter coefficients.

The present application discloses an audio noise reduction method, apparatus, system, and computer-readable storage medium. The audio noise reduction method includes: calculating the power spectrum of the data to be noise-reduced; initializing noise estimation parameters based on the power spectrum to obtain the noise spectrum of the noise data; performing minimum value tracking on the initialized noise estimation parameters in a first time period to obtain a first array; calculating the posterior signal-to-noise ratio and the prior signal-to-noise ratio of the data to be noise-reduced based on the first array; performing minimum value tracking on the initialized noise estimation parameters in a second time period to obtain a second array; calculating a gain estimation value of the noise-free data based on the second array, the unvoiced probability estimation value, the voiced probability estimation value, and the noise power spectrum estimation value; and performing noise reduction processing on the data to be noise-reduced based on the gain estimation value, the noise spectrum, and the noise power spectrum estimation value to obtain the noise-free data. By the above method, the present application can improve the noise reduction effect on audio data.

The invention provides a signal noise reduction method and a gas concentration detection method based on an improved threshold function. The signal noise reduction method comprises the following steps: decomposing an original signal containing noise into a plurality of signal components; wavelet threshold noise reduction processing is carried out on at least part of the signal components, and wavelet coefficients are processed through an improved threshold function in the processing process; and performing signal reconstruction based on a wavelet threshold noise reduction processing result to obtain a de-noised signal. A threshold function containing an exponential attenuation item is introduced, the function is continuous and smooth at a threshold point, when the amplitude of a wavelet coefficient is just larger than a threshold, the function can provide smooth shrinkage similar to a soft threshold, and signal oscillation caused by a hard threshold function is effectively avoided; when the amplitude of the wavelet coefficient is far greater than a threshold value, the exponential attenuation item rapidly approaches zero, so that the processed coefficient infinitely approaches the original coefficient; noise with small amplitude can be filtered out, high-fidelity preservation can be carried out on a large-amplitude coefficient, and the signal noise reduction precision and the waveform fidelity are remarkably improved.

The invention relates to a bristle number and integrity detection method based on pixel features and a self-adaptive threshold value, which comprises the following steps of: firstly, acquiring an initial image through the combination of bottom main backlight and side auxiliary backlight, then converting a color image into a grey-scale image, performing smoothing processing, positioning by adopting a morphological opening operation and eliminating reflective interference, and finally obtaining a bristle number and integrity detection result. The method comprises the following steps: performing four-direction edge detection fusion, brightening an image to four corners, calculating edge definition, outputting a qualified brightened image after optimization, performing block calculation on the image to calculate four-direction gradient and characteristic related parameters of each pixel, subtracting a mean noise map from the brightened image to obtain an illumination uniform correction image, determining a threshold value by adopting an between-class variance method, and outputting a final correction image. Bristles and background pixels are distinguished, connected domain detection statistics is carried out on the finally judged white pixels, and the number and integrity of the bristles are judged; the method effectively resists the detection of the white pixels of the bristles caused by the interference such as light reflection and uneven illumination, and can adaptively adjust the parameters to adapt to the detection of the bristles of different materials and specifications.

The present application relates to the technical field of image data processing, and more particularly to a kind of based on fractional order total variation GM-APD laser radar range image denoising algorithm.The algorithm includes the following steps: step S1, original data is estimated by maximum likelihood estimation algorithm pixel by pixel distance parameter, realize the extraction of GM-APD range image;Step S2, introduce fractional order differential operator to construct FOTV model, obtain the spatial relationship and similarity relationship between pixels using spatial kernel function and value domain kernel function to optimize fractional order differential operator, construct FOTVGM-APD laser radar denoising model based on spatial kernel function and value domain kernel function;Step S3, split Bregman algorithm is used for range image denoising.The present application realizes the suppression of GM-APD laser radar range image lost information and distance abnormal value noise, while retaining target details and contour information.