In an embodiment of the present inventive concept, there is provided an 0 smoothing method performed on the basis of deep gradient prior information to improve sharpness of an image by an image quality improving device, and the method comprises: a gradient-improved image generation step of generating a gradient-improved image by minimizing the gradients of pixels of an original image, by the image quality improving device; and a smoothing-improved image generation step of generating a smoothing-improved image smoothing-processed through one-step (.sub.0) estimation on the gradient-improved image, by the image quality improving device.

The present invention relates to edge restoration. In order to improve a restoration of the artificially created cleansed edges, an apparatus is proposed to automatically restore image edges after digital subtraction of digital material substitution to optimally resemble image edges in unmodified locations. The appearance of edges is machine-learned in an unsupervised non-analytical way from unmodified locations, and then, after digital suppression or digital material substitution, applied to the artificially created cleansed edges.

A machine learning based image processing architecture and associated applications are disclosed herein. In some embodiments, a machine learning framework is trained to learn low level image attributes such as object/scene types, geometries, placements, materials and textures, camera characteristics, lighting characteristics, contrast, noise statistics, etc. Thereafter, the machine learning framework may be employed to detect such attributes in other images and process the images at the attribute level.

An electronic device includes a first camera having a first view angle; a second camera having a second view angle that is smaller than the first view angle; a display; a memory; and a processor configured to: apply a first tuning parameter to first image data obtained by the first camera; recognize an external object; control, by driving the second camera, a center of the second view angle to be oriented toward the recognized external object; and apply, to the first image data, a second tuning parameter corresponding to a location of the center of the second view angle.

Systems and methods are provided for improving ultrasound image quality. In some embodiments, data sets can be formed from received echoes at each of a plurality of receive elements. The data between the data sets can be masked to include or exclude data. The masked data sets can then be beamformed to form ultrasound images.

Systems and methods for image noise reduction are provided. The methods may include obtaining first image data, determining a restriction or a gradient of the first image data, determining a regularization parameter for the first image data based on the restriction or the gradient, generating second image data based on the regularization parameter and the first image data, and generating a regularized image based on the second image data.

Disclosed are systems, methods, and computer-readable storage media to modify image content. One aspect includes identifying, by one or more electronic hardware processors, an image and content within the image, determining, by the one or more electronic hardware processors, a sky region of the image, determining, by the one or more electronic hardware processors, whether the content within the image is located within the sky region of the image, and in response to the content being within the sky region of the image, modifying, by the one or more electronic hardware processors, the content based on fractal Brownian motion.

Devices, methods, and non-transitory program storage devices for spatiotemporal image noise reduction are disclosed, comprising: maintaining an accumulated image in memory; and obtaining a first plurality of multispectral images (e.g., RGB-IR images). For each image in the first plurality of multispectral images, the method may: calculate a multispectral guide image for the current image; calculate blending weights for the current image; apply the calculated blending weights to each channel of the current image to generate a denoised current image; and update the accumulated image based on pixel differences between the denoised current image and the accumulated image. In some embodiments, additional images (e.g., the accumulated image and/or other images captured prior to or after a given current image) may also be included in the denoising operations for a given current image. Finally, the method may generate a denoised output image for each input image, based on the updated accumulated image.

The invention discloses a method for experimentally determining the conductivity of unpropped fracture in hydraulic fracturing of shale reservoir, comprising the following steps: select an outcrop sample with natural fractures in a shale reservoir section, and cut the outcrop sample with natural fractures along the extension direction of the natural fracture into no less than 8 square rock slabs; use a laser scanner to obtain the rough topography data of the fracture surface of each square rock slab and calculate the area tortuosity; pick out the rock samples; model the rough surface of the selected rock samples; import the treated surface model into the engraving machine, and select the downhole core or outcrop rocks in the same horizon for repeated production; calculate the shear slippage at different positions of unpropped fracture according to the data of shale reservoir; finally test the conductivity of the shale rock samples after shear slip.

An image adjustment method includes: sequentially processing a plurality of pixels in at least one frame, wherein a pixel under processing is a current pixel, the current pixel and multiple adjacent pixels form a current block, and each current block is performed with following operations: reading a grayscale value of each pixel in the current block; determining a region grayscale value and a region variance of the current block according to the grayscale values of the pixels; generating a variance adjustment parameter via a variance adjustment function, wherein the region grayscale value is a variable of the variance adjustment function; generating an adjusted region variance according to the variance adjustment parameter and the region variance; and comparing the adjusted region variance with a variance threshold to determine whether to perform a noise suppression operation on the current pixel.