3results about How to "Good details" patented technology

This utility model discloses a 3D scanner with replaceable filters, comprising: an instrument body; a camera unit and a laser projection unit disposed within the instrument body, wherein the camera in the camera unit and the projection surface in the laser projection unit are both disposed on the measurement surface of the instrument body; and a supplementary lighting unit located around the camera and disposed on the measurement surface, wherein the filter unit in the supplementary lighting unit is detachably disposed at the front end of the light source assembly in the supplementary lighting unit. This utility model significantly improves the scanner's adaptability to different material surfaces and scanning quality through the design of replaceable filter units, optimizes image clarity and detail, and allows for flexible adjustment and rapid replacement of filters according to the characteristics of different materials.

The application discloses a low-contrast image joint enhancement and super-resolution reconstruction method, which comprises the following steps: firstly, performing multi-scale Gaussian filtering on the low-contrast image; secondly, expanding the imaging dynamic range of the quotient image by using an S function; thirdly, performing color correction on the expansion result to obtain a contrast-improved image; and finally, optimizing the resolution and detail information of the contrast-improved image by a blind super-resolution reconstruction model constrained by a total variation to obtain a detail information and resolution improvement result. The low-contrast image joint enhancement and super-resolution reconstruction can help to overcome the influence of image adverse factors, such as low contrast / resolution and fuzzy details, and lay a foundation for image feature extraction and image interpretation.

The application provides an image super-resolution processing method and device, electronic equipment and storage medium. The method comprises the following steps: receiving a user input image to be processed; inputting the image to be processed into a lightweight neural network model, extracting detailed features of the image to be processed by the lightweight neural network, and fusing the extracted detailed feature map and the image to be processed to obtain a super-resolution image corresponding to the image to be processed. The user can directly process image data online on the terminal device, and the lightweight neural network composed of a convolution layer, a plurality of structure blocks and an up-sampling layer can greatly improve the image processing speed, avoid time-consuming network transmission and achieve real-time image processing.