A method of processing an image of a scene including a road acquired by a vehicle-mounted camera to generate boundary data indicative of a boundary of an image region which represents an unoccupied area of the road, comprising: generating an LL sub-band image of an N.sup.th level of an (N+1)-level discrete wavelet transform, DWT, decomposition of the image by iteratively low-pass filtering and down-sampling the image N times, where N is an integer equal to or greater than one; generating a sub-band image of an (N+1).sup.th level by high-pass filtering the LL sub-band image of the N.sup.th level, and down-sampling a result of the high-pass filtering, such that the sub-band image of the (N+1).sup.th level has a pixel region having substantially equal pixel values representing the unoccupied area of the road in the image; and generating the boundary data by determining a boundary of the pixel region.

The disclosure relates to a method for compressing in a packed data set a color image comprising color image samples arranged in a reference grid according to a sampling pattern, and comprising the steps of (a) performing a decorrelative transform providing component images, (b) performing a number of discrete wavelet decompositions on said component images and (c) forming packets in the packed data set by processing each consecutive line in the reference grid and each component and grouping all samples of said component in said line in one or more packets, and appending said one or more packets to the packed data set. The disclosure also relates to a device for compressing, a computer program for compressing, a method for decompressing, a device for decompressing, and a computer program for decompressing. The disclosure also relates to the packed data set produced by said method for compressing.

In one embodiment, an apparatus comprises a storage device and a processor. The storage device stores a plurality of images captured by a camera. The processor: accesses visual data associated with an image captured by the camera; determines a tile size parameter for partitioning the visual data into a plurality of tiles; partitions the visual data into the plurality of tiles based on the tile size parameter, wherein the plurality of tiles corresponds to a plurality of regions within the image; compresses the plurality of tiles into a plurality of compressed tiles, wherein each tile is compressed independently; generates a tile-based representation of the image, wherein the tile-based representation comprises an array of the plurality of compressed tiles; and stores the tile-based representation of the image on the storage device.

Systems and methods for integrating a three-dimensional X-ray computed tomography system with an independent sound wave system to determine mechanical properties of tissue using signals from the sound wave system. Methods are disclosed that generate a numerical simulation and take the transmitted wave signals as the optimization objective to estimate modulus distribution of the tissue. Further, the mechanical properties of the tissue are reconstructed based on an inverse algorithm.

A computer implemented method of decoding a signal. The method includes receiving a signal (which may be an electromagnetic signal), sampling the received signal to generate an input waveform having magnitude and phase components, applying a transform operation to the input waveform to generate a first decoded signal, and outputting the first decoded signal. The transform operation includes pre-processing the input waveform to generate a mirrored inverted waveform and applying a continuous wavelet transform to the mirrored inverted waveform to generate the first decoded signal. This allows inversion of the frequency and temporal resolution of the continuous wavelet transform, thereby enabling improved temporal and frequency decoding of a signal. The method is particularly suitable for signal filters and filtering units.

A method, apparatus, and a non-transitory computer-readable storage medium for image denoising. The method may include obtaining a raw image captured by a camera. The method may also include obtaining a color modeled image based on the raw image. The method may further include obtaining a subsampled raw image based on the raw image. The method may also include obtaining a denoised image based on a neural network processing the color modeled image and the subsampled raw image.

An image processing apparatus includes a first filter unit and a first composition unit. The first filter unit generates a plurality of results of filter processing for brightness by individually applying each of a plurality of filters for brightness with different frequency bands to a first brightness component image indicating a distribution of brightness components in a first image. The plurality of filters for brightness are spatial filters based on a visual characteristic of a human related to contrast detection. The first composition unit generates brightness contrast influence information indicating a distribution of degrees of influence of brightness contrast in the first image by compositing together the plurality of results of filter processing for brightness.

Systems and methods for realizing practical applications of high speed digital image correlation (DIC) for dynamic quantities of interest are provided. In particular, a series of images are captured for a component of interest in which a non-filtered sensor and an analog low-pass filtered sensor are included within the region of interest for the series of images. Displacement signals are obtained for the component of interest, the non-filtered sensor, and the analog low-pass filtered sensor by applying digital image correlation processing to the series of images, which may also be wavelet filtered. Dynamic quantities of interest may be generated and derived from the displacement signals after having been wavelet filtered. Such dynamic quantities of interest based on the wavelet filtered DIC-derived displacement signal may be compared to sensor-derived dynamic quantities of interest to determine if aliasing is or is likely to be present.

A computer-implemented method for evaluating colonoscopy performance includes: (S1) splitting a video acquired during a colonoscopy examination into a plurality of colonoscopy images; (S2) assigning each of the colonoscopy images into a fold-inspection group or a non-fold-inspection group according to a first classification criterion and a second classification criterion, wherein the first classification criterion comprises at least one of clarity, exposure, level of tissue wrinkling, and level of occlusion in each of the colonoscopy images; and the second classification criterion comprises at least one of an amount of haustrum, an amount of colonic lumen, and a position of the colonic lumen in each of the colonoscopy images; and (S3) determining a performance rating of the colonoscopy examination according to an elapsed time of the fold-inspection group. The method classifies colonoscopy images more accurately and reliably, thereby providing an effective tool for quality assessment and guidance of colonoscopy examinations.

An image processing apparatus includes a discrete wavelet transform (DWT) device that performs DWT and down sampling for a first source image to divide the first source into a low-frequency sub-image including a low-frequency component in a horizontal direction and a vertical direction and a plurality of high-frequency sub-images including a high-frequency component in at least one of the horizontal direction or the vertical direction, a frame memory storing a low-frequency sub-image of a second source image, a first noise reduction device that reduces noise in the low-frequency sub-image of the first source image using the low-frequency sub-image of the second source image, and an inverse discrete wavelet transform (IDWT) device that applies IDWT to the low-frequency sub-image of the first sub-image, which is reduced in noise through the first noise reduction device, and the high-frequency sub-images of the first image to restore the first source image.