A method and system are provided. The method includes receiving an input frame, performing high frequency noise reduction (HFNR) on the received input frame, performing low frequency noise reduction (LFNR) on the received input frame, and obtaining a hybrid denoised clean frame by fusing an output of the performed HFNR and an output of the performed LFNR.

Disclosed herein is method and fault detection system for detecting faults in one or more products. In an embodiment, method comprises generating plurality of wavelet coefficients corresponding to transformed images of each of the one or more products and determining a set of invariant features from the plurality of wavelet coefficients. Further, a dynamic set of invariant features is generated by grouping invariant features into a set of groups based on type of the one or more products. Subsequently, the dynamic set of invariant features is quantized based on a predetermined quantization threshold and a representative coefficient signature is associated for each group in the dynamic set of invariant features. Finally, faults in the one or more products are detected by comparing coefficient signatures associated with the one or more products with the representative coefficient signature of each group in the dynamic set of invariant features. In an embodiment, the present disclosure helps in accurate detection of faults in one or more products irrespective of type and characteristics of one or more products.

In various embodiments, an experiment analysis application detects executional artifacts in experiments involving microwell plates. The experiment analysis application computes one or more sets of spatial features based on one or more heat maps associated with a microwell plate. The experiment analysis application then aggregates the set(s) of spatial features to generate a feature vector. The experiment analysis application inputs the feature vector into a trained classifier. In response, the trained classifier generates a label indicating that the microwell plate is associated with a first executional artifact.

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.

An image processing method includes analyzing a first image including a face image in a plurality of directions, including recognizing photographing background information of the first image and brightness information and makeup information that are of a face, so that skin beautification processing can be purposefully performed on the face image comprehensively based on specific background information of the image, actual brightness distribution of the face, and actual makeup of the face.

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.

Disclosed is a method and a device for characterizing, for example identifying at least one object depicted in a raster image (1) or determining the speed of sound of the object, the raster image (1) having pixel rows and pixel columns. In order to efficiently and accurately characterize the object, the invention provides that several pixel columns (Cn) are selected and each of the selected pixel columns (Cn) is converted into a line profile (L), the amplitude of the line profile (L) representing the value (V) of image information of selected pixels of the respective selected pixel column (Cn), wherein the method comprises determining characteristics of the line profiles (L) and using the characteristics to characterize the at least one object depicted in the raster image (1).

The present invention provides a method of computer analysis of a data set representing an image to extract a texture based measure therefrom, said image including a multiplicative bias in intensity within the image of unknown magnitude, the method comprising applying to said data set a bank of texture extracting filters, such that said filters are chosen from filters that are invariant to the presence of a multiplicative bias field. By providing such a method, rather than attempting to correct the bias field before extraction of a texture based-biomarker, a texture-based biomarker that is bias field invariant is extracted. This makes correction of the bias field unnecessary.

A method and system are provided. The method includes determining a difference map between a reference frame and a non-reference frame, determining a local variance of the reference frame, determining a detail power map based on a difference between the determined local variance and the determined difference map, and determining a detail grade map based on the determined detail power map.

An image enhancement method includes: acquiring an image in a YUV format (110); performing N max layers of wavelet decomposition on a brightness component and chrominance components respectively (120); starting from the (N max)th layer, performing wavelet reconstruction on the low frequency sub-bands of the chrominance components of each layer on which edge preserving filtering has been performed according to the low frequency sub-band of the brightness component of the corresponding layer, and the high frequency sub-bands of the chrominance components continuously towards an upper layer until an image of an original size is obtained (130); performing, according to the brightness component, edge preserving filtering on the chrominance components that have been subjected to the wavelet reconstruction (140); integrating the chrominance components that have been subjected to the edge preserving filtering and the brightness component (150).