A computer-implemented method of automated X-ray inspection during the production of printed circuit board, PCB, assemblies. The method includes capturing an X-ray image of a PCB assembly, determining a first error indicator based on image processing of the captured X-ray image, determining, in case the first error indicator indicates the PCB assembly as faulty, a second error indicator based on the captured X-ray image using a trained adaptive algorithm, and outputting the second error indicator as a result of the inspection.

Wavelet transformation is performed on first image data and second image data until a decomposition level becomes a decomposition level based on synthesis control data or the like, and first wavelet coefficient data and second wavelet coefficient data are thereby generated. An ROI coefficient related to an ROI and a non-ROI coefficient in the first wavelet coefficient data are determined on the basis of mask data and the ROI coefficient in the first wavelet coefficient data and a wavelet coefficient in the second wavelet coefficient data are synthesized with each other, and synthesized coefficient data are thereby generated. Inverse wavelet transformation is performed on the synthesized coefficient data until a decomposition level becomes a predetermined end level, and synthetic image data are thereby generated.

In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

A method for generating a color-faithful extended-depth-of-field (EDF) image from a color volume of 2D images acquired at different focal depths using a microscope. The method involves: generating a grayscale volume; applying invertible color-to-grayscale transformation to the volume; applying wavelet transform to the grayscale volume to obtain a 3D wavelet-coefficient-matrix (WCM); selecting wavelet coefficients using a coefficient selection rule; generating a 2D-WCM and a 2D coefficient-map (CM); applying inverse transformation of the wavelet transform to the 2D-WCM to obtain a 2D grayscale EDF image; generating a 2D color-composite(CC) image; applying inverse transformation of the color-to-grayscale transformation to the 2D grayscale EDF image to obtain a 2D color EDF image; converting the 2D-CC image and the 2D color EDF image into a color space including chromaticity and intensity component(s); and concatenating, chromaticity component(s) of the 2D-CC image and intensity component(s) of the 2D color EDF image, to obtain a color-faithful EDF image.

Described herein is an image processing apparatus (701) comprising one or more processors (704) configured to: receive (601) a plurality of input images (301, 302, 303); for each input image, form (602) a set of decomposed data by decomposing the input image (301, 302, 303) or a filtered version thereof (307, 308, 309) into a plurality of frequency-specific components (313) each representing the occurrence of features of a respective frequency interval in the input image or the filtered version thereof; process (603) each set of decomposed data using one or more convolutional neural networks to form a combined image dataset (327); and subject (604) the combined image dataset (327) to a construction operation that is adapted for image construction from a plurality of frequency-specific components to thereby form an output image (333) representing a combination of the input images. The resulting HDR output image may have fewer artifacts and provide a better quality result. The apparatus is also computationally efficient, having a good balance between accuracy and efficiency.

A processing method for enhancing the image quality of an image, more particularly a digital medical grey scale image, that comprises the steps of a) decomposing an original image into multiple detail images at different resolution levels and/or orientations, b) processing the detail images to obtain processed detail images, c) computing a result image by applying a reconstruction algorithm to the processed detail ages, said reconstruction algorithm being such that if it were applied to the detail images without processing, then said original image or a close approximation thereof would be obtained, the processing of the detail images comprises the steps of: d) calculating at least one conjugate detail image, and e) computing at least one value of the processed detail images as a function of said conjugate detail image and said detail images.

The present invention relates to a method of determining stock in an inventory. The method comprises obtaining one or more images comprising one or more objects. Further, estimating a three dimensional (3D) location of a Stock Keeping Unit (SKU) marker associated with each of one or more visible objects. Furthermore, determining a stacking pattern of the one or more objects for each level on the pallet using one of the 3D location of SKU marker and a learning model. Thereafter, detecting at least one of presence or absence of one or more undetected objects at each level based on the stacking pattern and the 3D location of the SKU marker. Finally, determining the stock in the inventory based on the presence or the absence of the one or more undetected objects and the one or more visible objects.

A method to compute a variable number of image planes, which are selected to better represent the scene while reducing the artifacts on produced novel views. This method analyses the structure of the scene by means of a depth map and selects the position in the Z-axis to split the original image into individual layers. The method also determines the number of layers in an adaptive way.

Disclosed by the present application are an image enhancement method and apparatus, a terminal device and a computer-readable storage medium. The image enhancement method comprises: obtaining an image to be processed; performing a wavelet transform operation on the image to obtain raw feature information of the image, the raw feature information comprising global contour feature information, transversal detail feature information, longitudinal detail feature information, and contrast detail feature information; inputting the raw feature information into a trained target network for processing to obtain corresponding reconstruction feature information, the reconstruction feature information comprising global contour reconstruction information, transversal detail reconstruction information, longitudinal detail reconstruction information, and contrast detail reconstruction information; performing an inverse wavelet transform operation on the reconstruction feature information to obtain a reconstructed image; the resolution of the reconstructed image is higher than the resolution of the image to be processed.

A method for optimizing image capture of a scene by a swarm of drones including a root drone and first and second level-1 drones involves the root drone following a predetermined trajectory over the scene, capturing one or more root keyframe images, at a corresponding one or more root drone orientations and root drone-to-scene distances. For each root keyframe image: the root drone generates a ground mask image for that root keyframe image, and applies that ground mask image to the root keyframe image to generate a target image. The root drone then analyzes the target image to generate first and second scanning tasks for the first and second level-1 drones to capture a plurality of images of the scene at a level-1 drone-to-scene distance smaller than the root drone-to-scene distance; and the first and second level-1 drones carry out the first and second scanning tasks respectively.