A radiographic image processing apparatus includes a hardware processor, which determines the intensity of an edge in a radiographic image obtained by radiographically imaging a subject, sets a weighting factor to be used in extracting a frequency component from the radiographic image according to a determination result of the edge intensity, extracts the frequency component from the radiographic image using the weighting factor having been set, multiplies the extracted frequency component by a scattered radiation content rate to estimate a scattered radiation component in the radiographic image, multiplies the estimated scattered radiation component by a scattered radiation removal rate to generate a scattered radiation image representing the scattered radiation component to be removed from the radiographic image, and performs scattered radiation correction on the radiographic image by subtracting the scattered radiation image from the radiographic image.

An image processing apparatus includes an acquirer configured to acquire a captured image generated through imaging by an optical system, a reconstruction processor configured to reconstruct a discretized point spread function of the optical system using coefficient data used to approximate the point spread function, and a sharpening processor configured to perform unsharp mask processing for the captured image based on information on the reconstructed point spread function. A discretization interval of the reconstructed point spread function is different according to an image height.

An image processing apparatus includes an acquirer configured to acquire a captured image generated through imaging by an optical system, a reconstruction processor configured to reconstruct a discretized point spread function of the optical system using coefficient data used to approximate the point spread function, and a sharpening processor configured to perform unsharp mask processing for the captured image based on information on the reconstructed point spread function. A discretization interval of the reconstructed point spread function is different from a pixel pitch in an image sensor used for the imaging.

One embodiment provides a method, including: receiving a plurality of communication events associated with a pixel of an imaging device; identifying a frequency associated with the communication events, wherein the identifying a frequency comprises determining a number of communication events occurring within a predetermined time interval or determining a mean time interval between the communication events; determining, from a plurality of pixels neighboring the pixel, a frequency range comprising an upper frequency limit and a lower frequency limit; resolving, from the identified frequency and the determined frequency range, whether the pixel comprises a non-conforming pixel; and masking, if the pixel comprises a non-conforming pixel, subsequent communication events from the non-conforming pixel. Other aspects are described and claimed.

The disclosed computer-implemented method may include (1) identifying, via at least one sensor, an object of interest located within a real-world environment, (2) mapping an area of the real-world environment surrounding the object of interest, (3) generating a virtual environment based on the mapped area of the real-world environment surrounding the object of interest, and (4) displaying, in real-time, the object of interest within the virtual environment. Various other methods, systems, and computer-readable media are also disclosed.

The invention relates to a visible light image and infrared image fusion processing system and a fusion method. The fusion processing system comprises an image acquisition module, an image fusion module and an image display module, wherein the image fusion module is connected with the image acquisition module and the image display module. By adoption of the fusion method, the fusion ratio of a visible light image to an infrared image can be adjusted according to requirements, and detailed images are filtered and compared and then enhanced, so that detail information of a fused image is improved, and noise interference is avoided. Furthermore, fusion weights of the visible light image and the infrared image and the detail enhancement degree can be flexibly controlled through external parameter adjustment, and thus various display requirements are met.

Systems and methods of enhancing the resolution of digital terrain models (DTM) for location-based applications and analyses. The DTM enhancement process takes the signature of the input image (e.g., via the input image and a noise surface file with similar characteristics as the sensor used to capture the input image) and applies it to the DTM without including large features such as buildings. The disclosed methods include utilize a process similar to that used for enhancing a DSM based on mapping the changing intensity from the image file to changes in elevation in the DSM using a regression over a local neighborhood of pixels. Further, the disclosed methods do not rely on information about the sensors and are extendable to be able to utilize any types of images. Additionally, the disclosed embodiments are sensor agnostic and can be applied on any type of image collected by any type of sensor.

A visual aid provides utility to its users by performing geometric transformations of raw images from a digital camera into processed images for electronic display, via three nested levels of implementation namely, the nature of the mapping that will be applied, i.e. specifying a specific mapping or parameterized family of mappings; the algorithm set used to effect the select mapping, independent of the processor upon which it executes; and the method for realizing this algorithm on that processor. Likewise offered for consideration are software feature sets tied to user interface which enable dynamic tuning of the subject visual context and environments for low vision users, Inter Alia.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that upsample and refine segmentation masks. Indeed, in one or more implementations, a segmentation mask refinement and upsampling system upsamples a preliminary segmentation mask utilizing a patch-based refinement process to generate a patch-based refined segmentation mask. The segmentation mask refinement and upsampling system then fuses the patch-based refined segmentation mask with an upsampled version of the preliminary segmentation mask. By fusing the patch-based refined segmentation mask with the upsampled preliminary segmentation mask, the segmentation mask refinement and upsampling system maintains a global perspective and helps avoid artifacts due to the local patch-based refinement process.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that upsample and refine segmentation masks. Indeed, in one or more implementations, a segmentation mask refinement and upsampling system upsamples a preliminary segmentation mask utilizing a patch-based refinement process to generate a patch-based refined segmentation mask. The segmentation mask refinement and upsampling system then fuses the patch-based refined segmentation mask with an upsampled version of the preliminary segmentation mask. By fusing the patch-based refined segmentation mask with the upsampled preliminary segmentation mask, the segmentation mask refinement and upsampling system maintains a global perspective and helps avoid artifacts due to the local patch-based refinement process.