5 results about "Exposure fusion" patented technology

An exposure control method includes: calculating a long-exposure brightness mean value of pixels of darker (N−X) kinds of brightness among N kinds of brightness values of a long exposure image, wherein a total number of pixels of brightest X kinds of brightness values among the N kinds of brightness values is a long-exposure bright pixel number; when the long-exposure brightness mean value doesn't approximate to a long-exposure target brightness mean value, adjusting exposure setting for generating / modifying the long exposure image to make the two brightness mean values be similar / equal; determining brightest K pixels of a short exposure image, wherein the K is equal to the long-exposure bright pixel number; calculating a short-exposure brightness mean value; and when the short-exposure brightness mean value doesn't approximate to a short-exposure target brightness mean value, adjusting exposure setting for generating / modifying the short exposure image to make the two brightness mean values be similar / equal.

The application belongs to the technical field of image processing, and particularly relates to a multi-exposure fusion method for separating illumination and motion; low-exposure images, medium-exposure images and high-exposure images are subjected to illumination preprocessing and input into a fusion model; the low-exposure images and the high-exposure images are mapped to the illumination level of the medium-exposure images and normalized according to first gamma values and second gamma values, to obtain first new medium-exposure images and second new medium-exposure images, and the new medium-exposure images are subjected to motion alignment with the medium-exposure images; the first new medium-exposure images and the second new medium-exposure images are subjected to inverse gamma transformation respectively, to obtain new low-exposure images and new high-exposure images, and the new low-exposure images and the new high-exposure images are subjected to illumination fusion with the medium-exposure images to obtain a high dynamic range image, loss is calculated, and model parameters are trained by back propagation; a to-be-processed image is obtained, and a corresponding high dynamic range image of the to-be-processed image is obtained by using the trained fusion model; the application adopts the idea of separating illumination and motion, so that the high dynamic range image retains more information of the input image, and ghosting is reduced.

This invention relates to the field of image enhancement technology and discloses a low-light image enhancement method and system for 5G remote control systems based on deep Retinex decomposition. The method acquires and preprocesses the original low-light image from the front-end device of the 5G remote control system, inputs it into a lightweight multi-scale illumination sensing network to separate the initial illumination and reflection components, corrects the initial illumination component using a dynamic dual-branch exposure fusion module, processes the initial reflection component using a residual noise-detail co-optimization module, and then reconstructs and enhances the image through a halo and color joint correction mechanism. The image is then encoded, compressed, and transmitted to the remote control center via a 5G private network. This invention solves the core problems of underexposure and overexposure, noise interference, detail loss, and wasted bandwidth in low-light images in 5G remote control scenarios, achieving lightweight real-time enhancement at the edge, ensuring image quality and transmission efficiency, and providing reliable support for remote control decisions.

This invention provides a multi-exposure image fusion system, method, and electronic device. The system includes: an image sequence alignment and correction module, comprising a selection submodule and an image sequence alignment and correction network. The selection submodule is configured to select a reference exposure level image, and the image sequence alignment and correction network is configured to align and correct other exposure level images based on the reference exposure level image; an image fusion deep network, configured to fuse the aligned and corrected multi-exposure image sequence to generate a multi-exposure fused image; and a visual quality-driven perception optimization module, configured to evaluate the multi-exposure fused image using a fused image visual quality evaluation model and optimize the image fusion deep network based on the evaluation results. This invention, by performing multi-exposure image fusion based on a deep network, can effectively capture image details and better understand the content of multi-exposure images, thereby achieving fast and efficient multi-exposure image fusion.

This invention discloses a real-time multi-exposure HDR image processing system based on FPGA, comprising: a DDR3 image storage control module, integrated within the FPGA, communicating with an external DDR3 chip via the AXI4 bus protocol, supporting high-speed storage and parallel reading of 8-channel multi-exposure images, and outputting synchronized multi-exposure image data and valid frame, line, and data signals; a multi-exposure HDR fusion module, connected to the output of the DDR3 image storage control module, employing a 6-stage hardware pipeline architecture to achieve pixel-level weighted fusion of 8-channel multi-exposure images, outputting a 24-bit RGB format HDR fusion image; and a Reinhard tone mapping module, connected to the output of the multi-exposure HDR fusion module, implementing dynamic range compression from HDR to LDR images based on an optimized Reinhard algorithm, outputting a 24-bit RGB format LDR image that meets the requirements of display devices. This invention achieves real-time processing of multi-exposure HDR images throughout the entire process through the integrated and synchronized optimization of these three modules.