A digital graduated filter is implemented in an imaging device by combining multiple images of the subject wherein the combining may include combining different numbers of images for highlights and for shadows of the subject. The imaging device may present a user with a set of pre-defined graduated filter configurations to choose from. A user may also specify the direction of graduation and strength of graduation in a viewfinder. In an alternative implementation, combining may include scaling of pixels being added instead of varying the number of images being combined. In an alternative implementation, the combining of multiple images may include combining a different number of images for highlights of the subject than for shadows of subject.

Smart metrology methods and apparatuses disclosed herein process images for automatic metrology of desired features. An example method at least includes extracting a region of interest from an image, the region including one or more boundaries between different sections, enhancing at least the extracted region of interest based on one or more filters, generating a multi-scale data set of the region of interest based on the enhanced region of interest, initializing a model of the region of interest; optimizing a plurality of active contours within the enhanced region of interest based on the model of the region of interest and further based on the multi-scale data set, the optimized plurality of active contours identifying the one or more boundaries within the region of interest, and performing metrology on the region of interest based on the identified boundaries.

An image processing method acquires an image, restores a saturated region in which a pixel in the image has a first reference value based on a first illuminance component of the image, enhances a dark region in which a value of a pixel in the image is less than a second reference value based on the restored saturated region and the first illuminance component, and outputs a dark region-enhanced image.

A method of generating a high dynamic range (HDR) image is provided that includes capturing a long exposure image and a short exposure image of a scene, computing a merging weight for each pixel location of the long exposure image based on a pixel value of the pixel location and a saturation threshold, and computing a pixel value for each pixel location of the HDR image as a weighted sum of corresponding pixel values in the long exposure image and the short exposure image, wherein a weight applied to a pixel value of the pixel location of the short exposure image and a weight applied to a pixel value of the pixel location in the pixel long exposure image are determined based on the merging weight computed for the pixel location and responsive to motion in a scene of the long exposure image and the short exposure image.

Systems and methods that facilitate efficient and effective shadow image generation are presented. In one embodiment, a hard shadow generation system comprises a compute shader, pixel shader and graphics shader. The compute shader is configured to retrieve pixel depth information and generate projection matrix information, wherein the generating includes performing dynamic re-projection from eye-space to light space utilizing the pixel depth information. The pixel shader is configured to create light space visibility information. The graphics shader is configured to perform frustum trace operations to produce hard shadow information, wherein the frustum trace operations utilize the light space visibility information. The light space visibility information can be considered irregular z information stored in an irregular z-buffer.

A method (10) utilizes first, second, and third image data originating from first, second, and third digital image frames, respectively, captured sequentially in time of a sample volume containing a fluid possibly comprising moving microscopic objects comprising moving microscopic objects while illuminating the sample volume by coherent light, each image data comprising, for a moving microscopic object of foreign object present in the sample volume, a hologram pattern (11); and comprises automatically generating first differential image data comprising the difference of the first and the second image data, (13a); automatically generating second differential image data comprising the difference of the second and the third image data (13b); automatically generating product of difference (POD) image data comprising the product of the first and the second differential image data, (14); and automatically detecting the presence of moving microscopic object(s) in the sample volume on the basis of product pattern(s) present in the POD image data (17).

The present disclosure provides a method, an electronic device, and a computer readable storage medium for image fusion. The image fusion method includes calculating a fusion coefficient image M based on a first frame image I.sub.1 or based on both the first frame image I.sub.1 and a second frame image I.sub.2; calculating a first gradient D.sub.1 of the first frame image I.sub.1 and a second gradient D.sub.2 of the second frame image I.sub.2; calculating a preliminary fusion result J based on the calculated fusion coefficient image M, the first gradient D.sub.1 and the second gradient D.sub.2; and obtaining an output image I.sub.3 based on the calculated fusion coefficient image M, the first gradient D.sub.1, the second gradient D.sub.2 and the preliminary fusion result J, wherein brightness of the first frame image I.sub.1 is greater than brightness of the second frame image I.sub.2, and wherein the fusion coefficient image M is used to mark fusion weights of pixels in the first frame image I.sub.1.

This disclosure provides an image processing apparatus comprising a first setting unit which sets a first parameter for processing for removing an influence of a fine particle component based on image data; a first image processing unit which performs fine particle removal processing based on the first parameter; a second setting unit which sets a second parameter; a second image processing unit which performs fine particle removal processing based on the second parameter; a setting unit which sets a region for which the first image processing unit is to be used and a region tor which the second image processing unit is to be used and a generation unit which generates image data by applying a result from the first image processing unit and a result from the second image processing unit to the respective set regions.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for adjusting a brightness of a display based on an image. In one aspect, a method includes obtaining an image, determining an amount of brightness in the image, determining an amount of contrast in the image, determining a brightness gain that reflects an amount to adjust brightness of a display based on both the amount of brightness in the image and the amount of contrast in the image, adjusting the brightness of the display based on the brightness gain, and providing the image for output on the display with the brightness of the display adjusted in accordance with the brightness gain.

A multi-label heat map generating system is operable to receive a plurality of medical scans and a corresponding plurality of global labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the global labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Heat map visualization data can be generated for transmission to a client device based on the probability matrix data that indicates, for each of the set of abnormality classes, a color value for each pixel of the new medical scan.