Abstract: An image processing apparatus that acquires a focusing distance from a plurality of images each having different degrees of blur, includes: a misalignment detection unit that detects misalignment among the plurality of images; a distance information acquisition unit that acquires, based on the plurality of images, distance information, which is information to indicate a focusing distance in an area of the images; and a reliability information acquisition unit that creates, based on the detected misalignment, reliability information, which is information to indicate reliability of the focusing distance acquired for each area, wherein the distance information acquisition unit changes the acquired distance information based on the reliability information.

Abstract: A computer-implemented method for automatically retrieving information regarding optical properties of a scattering medium including receiving a first digital image and a first image quality value associated with the first digital image and sharpness of an edge of the first digital image, producing an optimized image, transforming the optimized image into an optimized optical transfer function, receiving a second digital image and a second image quality value associated with the second digital image and sharpness of an edge of the second digital image, identifying either the first or second digital image as an optimized digital image, and transforming the optimized optical transfer function into an optimized value of the optical parameter.

Abstract: An image processing apparatus which estimates a point spread function (PSF) of at least one input image, and the image processing apparatus includes: an S/N ratio estimation unit which estimates an S/N ratio of the input image at each spatial frequency; a restricted range calculation unit which calculates a restricted range that is a range of the spatial frequency in which the frequency component of the input images are restricted and that more likely includes a spatial frequency in which the S/N ratio is lower; a frequency restriction unit which generates a restricted image by restricting a frequency component of the input image within the restricted range; and a PSF estimation unit which estimates the PSF of the input image, using the restricted image.

Abstract: An image processing method, for correcting a blur a due to an optical system of an image capturing apparatus, the image processing method including, storing a plurality of representative filters in a memory, selecting a subset of representative filters from the plurality of representative filters based on a pixel position of a pixel of interest in an image, applying each of the selected representative filter to a pixel value of the pixel of interest, and correcting the pixel value of the pixel of interest based on (a) a result of the application of filters, and (b) the pixel position of the pixel of interest.

Abstract: An image analysis apparatus for processing a 3D video signal comprising successive pairs of images representing different respective views of a scene to generate an image depth indicator comprises a correlator configured to correlate image areas in one of the pair of images with image areas in the other of the pair of images so as to detect displacements of corresponding image areas between the two images; a graphics generator configured to generate a graphical representation of the distribution of the displacements, with respect to a range of possible displacement values, across the pair of images; and a display generator for generating for display the graphical representation in respect of a current pair of images and in respect of a plurality of preceding pairs of images, so as to provide a time-based representation of variations in the distribution of the displacements.

Abstract: A hand-held or otherwise portable or spatial or temporal performance-based image capture device includes one or more lenses, an aperture and a main sensor for capturing an original main image. A secondary sensor and optical system are for capturing a reference image that has temporal and spatial overlap with the original image. The device performs an image processing method including capturing the main image with the main sensor and the reference image with the secondary sensor, and utilizing information from the reference image to enhance the main image. The main and secondary sensors are contained together within a housing.

Abstract: An image stabilized digital image capture device, comprising an image sensor for capturing a digital image; an optical system for imaging a scene onto the image sensor; an image stabilization system; an exposure control system; a memory system; and a processor. The processor is used to perform the steps of determining exposure settings using the exposure control system; selectively engaging the image stabilization system responsive to whether the determined exposure settings satisfy a predefined condition; capturing a digital image of a scene using the image sensor and the selectively engaged image stabilization system; and storing the captured digital image in the memory system.

Abstract: To allow a viewer to easily understand the details of a moving image shot by an image capturing apparatus in the case where the moving image is browsed. A camerawork detecting unit 120 detects the amount of movement of an image capturing apparatus at the time of shooting a moving image input from a moving-image input unit 110, and, on the basis of the amount of movement of the image capturing apparatus, calculates affine transformation parameters for transforming an image on a frame-by-frame basis. An image transforming unit 160 performs an affine transformation of at least one of the captured image and a history image held in an image memory 170, on the basis of the calculated affine transformation parameters. An image combining unit 180 combines, on a frame-by-frame basis, the captured image and the history image, at least one of which has been transformed, and causes the image memory 170 to hold a composite image.

Abstract: Disclosed is an image processing apparatus for converting an original image. Processing of blurring an input original image is performed to generate a blurred image. A difference image that is the difference between the original image and an adjusted image obtained by adjusting the density of the blurred image is generated. The difference image and the original image are composited based on the density of the difference image and the density of the original image. This allows to easily obtain a painting-like effect even in, for example, a low-contrast portion of an image.

Abstract: Example embodiments presented herein are directed towards rolling shutter video stabilization utilizing three dimensional motion analysis. In stabilizing the rolling shutter video, a three dimensional motion of the device capturing the image may be determined or estimated. In some example embodiments the three dimensional motion may be a purely rotational motion, a purely translation motion, or a rotational and translational motion. The estimated three dimensional motion may be utilized to adjust the pixels of an image frame in order to rectify and stabilize the video image.

Abstract: An apparatus includes an attention region determining unit and a blurring unit. The attention region determining unit is configured to determine an attention region of an image that includes an intended subject of the image. The blurring unit is configured to blur all of the image regions outside of the attention region.

Abstract: A method for rendering an image from a light-field camera, which generates a raw light-field image, includes: generating feature data, which includes feature elements associated with position information and obtained based on the raw light-field image and a preset threshold condition; generating a raw focused image from the raw light-field image; obtaining a virtual focus position that is designated on the raw focused image; and refocusing the raw focused image according to the virtual focus position by updating pixel values of pixels of the raw focused image that correspond respectively in position to the feature elements of the feature data, so as to generate a refocused image.

Abstract: A camera module is provided for an electronic device, such as a wireless mobile station or standalone device. The camera module includes a low F-stop lens assembly that is translatable into a desired position. An image of a subject is provided to an EDOF assembly, including a sensor assembly, and an extended-depth-of-field processor. The extended-depth-of-field processor provides for formation of a resultant, recorded image that exhibits improved depth of field characteristics.

Abstract: An image capturing apparatus includes: an imaging element; a main lens that condenses light from a subject toward the imaging element; a micro lens array that is configured by a plurality kinds of micro lenses with different focal lengths that is disposed between the imaging element and the main lens and causes light transmitted through the main lens to form an image on the imaging element; and a CPU that, in response to receiving a designation of a distance to a side of a subject that is photographed, performs weighting for each image that is imaged by the plurality kinds of micro lenses of the imaging element based on the distance so as to constitute one captured image.

Abstract: A classifier training system trains a classifier for evaluating image deblurring quality using a set of scored deblurred images. In some embodiments, the classifier training system trains the classifier based on a number of sub-images extracted from the scored deblurred images. An image deblurring system applies a number of different deblurring transformations to a given blurry reference image and uses the classifier trained by the classifier training system to evaluate deblurring quality, thereby finding a highest-quality deblurred image. In some embodiments, the classifier training system trains the classifier in the frequency domain, and the image deblurring system uses the classifier trained by the classifier training system to evaluate deblurring quality in the frequency domain. In some embodiments, the image deblurring system applies the different deblurring transformations iteratively.

Abstract: An image processing device that sets weighting factors for captured image data and interpolation image data region by region based on in-focus state information indicating in-focus states of the respective regions, and synthesizes the captured image data and the interpolation image data based on the weighting factors. A weighting factor for interpolation image data in an in-focus region is smaller than a weighting factor for interpolation image data in at least a part of a region other than the in-focus region.

Abstract: Computer-implemented arrangements for adjusting the focus in original electron microscope image data are described. In an implementation, a fractional Fourier transform operation and a phase restoration operation, both responsive to a fractional power, are collectively applied to original electron microscope image data to produce computationally-focused image data. A parameter adjuster is used to provide a range of variation of the power, and can be adjusted by a user or under the direction of a control system. The fractional Fourier transform operation and the phase restoration operation can be realized by at least one numerical algorithm and can comprise an approximation.

Abstract: An image editing method for editing an original image is provided. The original image includes at least a first object and a second object. The method includes steps of: obtaining a first distance between the first object and a lens; obtaining a second distance between the second object and the lens; obtaining a blur matrix set according to the first distance and an optical parameter; obtaining a first blur matrix from the blur matrix set according to the second distance; and performing a blur process on the second object according to the first blur matrix to generate a blurred second object, and generating a simulated image from the first object and the blurred second object.

Abstract: Methods, systems, and apparatus, including computer program products, for identifying regions of interest in an image and identifying a barcode in a degraded image are provided. A region of interest is identified by pre-processing an image, generating a binary image based on a metric calculated on the pre-processed image, and analyzing regions of the image identified using connected components and other analysis. A barcode is identified by searching a population of barcodes, degrading ideal image intensity profiles of candidate barcodes, and comparing the degraded ideal image intensity profiles to an image intensity profile of the degraded image.

Abstract: An image processing apparatus and image processing method capable of providing a high-resolution image for a desired position by use of light field data are provided. The image processing apparatus may determine a position of a portion of light field data among light field data corresponding to a scene, according to a desired focusing position, generate a refocusing first image by use of the position determined portion of light field data, generate a high-resolution image having a predetermined setting ratio relative to the refocusing first image, determine a ratio for local synthesis between the high-resolution image and an enlarged image of the refocusing first image by use of a similarity with respect to the position determined portion of light field data, and generate a synthesized image by synthesizing the high-resolution image and the enlarged image of the refocusing first image according to the local synthesis ratio.

Abstract: There is provided an image processing device including a focus area setting section, a depth acquisition section, and a blur processing section. The focus area setting section sets each of a plurality of areas as a focus area, and the each of the plurality of areas is intended to be kept in focus in a captured image of a subject. The depth acquisition section acquires a depth of the subject in relation to each pixel in the image. The blur processing section sets each of the pixels in the image as a target pixel and performs a blur process on the target pixel in accordance with a minimum value of absolute value of each depth difference between depth corresponding to the target pixel and depth corresponding to each of the focus area.

Abstract: An auto-focus image system that includes a pixel array coupled to a focus signal generator. The pixel array captures an image that has a plurality of edges. The generator generates a focus signal that is a function of a plurality of edge-sharpness measures, each being measured from a different one of the plurality of edges. The edge-sharpness measure is a quantity that has a unit that is a power of a unit of length. It may be a distance in the edge. It may be an area. It may be a central moment. The generator may reduce a relative extent to which an edge contributes to the focus signal on basis of detecting that the edge does not have sufficient reflection symmetry in a sequence of gradients of an image signal across the edge according to a predefined criterion. The edge may be prevented from contributing altogether.

Abstract: An image processing apparatus includes a point spread function (PSF) pattern generation unit for generating a PSF pattern in which a plurality of PSFs are located in a plurality of lines of the PSF pattern; a PSF estimation unit for estimating PSFs of an out-of-focus input image from step responses of the plurality of lines of the PSF pattern with respect to an edge of the out-of-focus input image; and an image restoration unit for restoring the out-of-focus input image to a focused restored image using the estimated PSF.

Abstract: An apparatus which provides a multiplicity of test colors and neutral tones simultaneously within a microscope's optical system that may be used to align, and/or calibrate the microscope, and the light source and any associated recording devices, automatically or with input from an operator. The test colors and neutral tones may also be used as references against which to judge specimens being viewed in the microscope. Also disclosed is a method of operating a microscope using such a such pattern.

Abstract: In an image capturing and processing system, a device and method is provided. The lens is made of simple lens with high diffractive materials and known strong color aberrations. The method includes the steps of: calibrating or measuring a Point Spread Function (PSF) for each color components at a set of distances, capturing image data, and calculating the image obtained using a de-convolution method based on the PSF found.

Abstract: Estimating focus error in an image involves a training phase and an application phase. In the training phase, an optical system is represented by a point-spread function. An image sensor array is represented by one or more wavelength sensitivity functions, one or more noise functions, and one or more spatial sampling functions. The point-spread function is applied to image patches for each of multiple defocus levels within a specified range to produce training data. Each of the images for each defocus level (i.e. focus error) is sampled using the wavelength sensitivity and spatial sampling functions. Noise is added using the noise functions. The responses from the sensor array to the training data are used to generate defocus filters for estimating focus error within the specified range. The defocus filters are then applied to the image patches of the training data and joint probability distributions of filter responses to each defocus level are characterized.

Abstract: A dual sensor camera that uses two aligned sensors each having a separate lens of different focal length but the same f-number. The wider FOV image from one sensor is combined with the narrower FOV image from the other sensor to form a combined image. Up-sampling of the wide FOV image and down-sampling of the narrow FOV image is performed. The longer focal length lens may have certain aberrations introduced so that Extended Depth of Field (EDoF) processing can be used to give the narrow FOV image approximately the same depth of field as the wide FOV image so that a noticeable difference in depth of field is not see in the combined image.

Abstract: Image blurring of a mass spectrum image obtained by the signal intensity for each mass/charge ratio from the obtained two-dimensional mass spectrum is reduced by image restoration using a blurring function. The blurring function expresses a two dimensional distribution of transferred energy related to the distance from a point of primary ion incidence limited by a region exceeding desorption energy of a molecule.

Abstract: A method for deblurring a blurry image (18) includes utilizing a spatial mask and a variable splitting technique in the latent sharp image estimation cost function. Additionally or alternatively, the method can include the utilizing a spatial mask and a variable splitting technique in the PSF estimation cost function. The spatial mask can be in a regularization term of either or both the latent sharp image estimation cost function and the PSF cost function. The latent sharp image estimation cost function can be used for non-blind deconvolution. Alternatively, one or both cost functions can be used for blind deconvolution.

Abstract: For each pixel of an input image, a direction toward which variation in pixel value between the relevant pixel and its peripheral pixels is largest and an amount of the variation in pixel value are computed, where a direction unit vector that indicates the direction toward which the variation is largest and a variation vector that represents the amount of the largest variation in the pixel value are computed. For each pixel, a regularization strength is computed utilizing the direction unit vector, the variation vector, and a regularization strength computing formula so that the larger the amount of the variation in pixel value toward a direction indicated by the direction unit vector, the less the regularization strength. An optimization function is determined based on the input image and the regularization strength for each pixel thereof and assigns a value for each pixel, by which the determined optimization function has the minimum value, to the corresponding pixel of a restored image to be generated.

Abstract: A method for correcting image data, in particular for color correction and cross-talk reduction of image data obtained by an image sensor comprising image pixels (R, G, B) and non-image pixels (F), the image pixels (R,G,B) being overlaid with a color filter array (CFA) and having a different spectral sensitivity than the non-image pixels, comprises determining a set of color components (rold, gold, bold) for the image pixels (R,G,B) based on raw image data (rraw, graw, braw) read only from the image pixels (R,G,B) of the image sensor (10); correcting the set of color components of non-direct neighbors of the non-image pixels (F) by means of a first color correction matrix (CCM1); and correcting the set of color components of direct neighbors of the non-image pixels by means of a second color correction matrix (CCM2) different from the first color correction matrix. An image sensor (10) and an auto-focus camera using same are also disclosed.

Abstract: A method for deblurring a blurry image (18) includes utilizing a spatial mask and a variable splitting technique in the latent sharp image estimation cost function. Additionally or alternatively, the method can include the utilizing a spatial mask and a variable splitting technique in the PSF estimation cost function. The spatial mask can be in a fidelity term in either or both the latent sharp image estimation cost function and the PSF cost function. The latent sharp image estimation cost function can be used for non-blind deconvolution. Alternatively, one or both cost functions can be used for blind deconvolution.

Abstract: An image de-blurring system obtains a blurred input image and generates, based on the blurred input image, a blur kernel. The blur kernel is an indication of how the image capture device was moved and/or how the subject captured in the image moved during image capture, resulting in blur. Based on the blur kernel and the blurred input image, a de-blurred image is generated. The blur kernel is generated based on sharp versions of the blurred input image predicted using a data-driven approach based on a collection of prior edges.

Abstract: A system and method for distributing at least one digital photographic image is presented, the system and method comprising at least one capturing device and at least one receiving device disposed in a communicative relation with one another via at least one wireless network. In particular, the capturing device is structured to capture the at least one digital photographic image via, for example, a capture assembly, whereas the receiving device is cooperatively structured to receive the digital photographic image via, for example, the at least one wireless network. In addition, the capturing device(s) and receiving device(s) may be disposed in a selectively paired relationship via one or more common pre-defined pairing criteria. Further, the at least one digital photographic image may be filtered via at least one pre-defined transfer criteria disposed on the capturing device and/or receiving device.

Abstract: Computer-implemented arrangements for adjusting the focus in original electron microscope image data are described. In an implementation, an origin-centered fractional Fourier transform operation and an origin-centered phase restoration operation, both responsive to a provided fractional power value, are collectively applied to original electron microscope image data to produce computationally-focused image data. A parameter adjuster is used to provide a range of variation of the power value, and can be adjusted by a user or under the direction of a control system. The fractional Fourier transform operation and the phase restoration operation can be realized by at least one numerical algorithm and can comprise an approximation.

Abstract: A method of detecting the subject of an image and imaging device thereof are disclosed. The method comprises the following steps of providing an image capturing module to capture the temporal image, providing the image processing module to receive the temporal image and to determine a plurality of objects from the temporal image, using the image processing module to define a plurality of regions on the temporal image based on the center of temporal image, using the image processing module to assign an initial setup value to each of the objects according to the region corresponding to each of the objects, using the imaging processing module to execute an operation to increase or decrease each of the initial setup values, using the image processing module to execute a sorting, and selecting at least one of the objects as a subject of the temporal image.

Abstract: A method and apparatus (70) is provided for estimating blur degradation of an image (75) that includes a specular highlight. A specular highlight in the image is first identified (77), for example with user assistance. A blur kernel is then derived by extracting (78A) the identified specular highlight as foreground from the image (75) and using the extracted foreground as the blur kernel (78C). The image (75) can then be deblurred by deconvolution (79) with the derived blur kernel.

Abstract: Methods for correcting distortions in an image including text, or an image of a page that includes text, are disclosed. The methods include identifying reliable and substantially straight lines from elements in the image. Vanishing points are determined from the lines. Parameters associated with a rectangle are determined. A coordinate conversion is performed.

Abstract: A projector includes: a frame image storage unit that stores input image data input to the projector; a block image storage unit that stores a part of the input image data in terms of block image data including N×M (where N and M?2) pixels; a correction processing unit that performs a correction process of correcting a distortion of the image projected onto the projection plane to generate corrected image data which is image data after correction on the basis of the block image data stored in the block image storage unit; and a block image predicting unit that, while the correction processing unit performs the correction process on a predetermined pixel, predicts the block image data necessary for the correction process on a pixel to be processed after the predetermined pixel.

Abstract: There is provided an image processing apparatus including: an exposure compensation unit carrying out exposure compensation of a long exposure image and a short exposure image in keeping with an exposure ratio; a blur information measuring unit using the exposure-compensated images to calculate blur information showing a degree of discrepancy between pixel values at a corresponding pixel position in the long exposure image and the short exposure image; a blending coefficient calculating unit using the blur information to decide a blending coefficient to be used in a blending process on pixel values at the corresponding pixel position in the long exposure image and the short exposure image; and a blending unit carrying out the blending process on the pixel values at the corresponding pixel position in the long exposure image and the short exposure image using the calculated blending coefficient to decide the pixel value of an output image.

Abstract: A feature-based method and system for blur estimation in eye images. A blur estimation can be performed from eye/iris images in order to produce de-blurred images that are more useful for biometric identification. The eye/iris region, in particular the edge between the iris and pupil regions, can be utilized. The pattern of shutter motion or a characterization of the optical system can be utilized. By capturing a burst of images, or a video stream, one can use eye position in the images before and after a given capture to predict the motion of the eye within that capture. Because the before/after image frames need only contain the information necessary to locate the eye, and need not contain sufficient information to perform matching, the capture of these images can be accomplished with a wider range of settings.

Abstract: Aspects of the subject technology relate to methods and systems for removing haze from an input image. The system includes a polarimeter configured to receive an input image, the input image comprising haze corresponding to light scatter. The polarimeter is further configured to produce plural Stokes values based on received input image. The system also includes a signal processor coupled to the polarimeter. The signal processor is configured to determine a peak angle and a valley angle for the input image based on the plural Stokes values, where the peak angle corresponds to an angle at which the input image has the most amount of light scatter passing therethrough, and the valley angle corresponds to an angle at which the input image has the least amount of light scatter passing therethrough. The signal processor is further configured to perform removal of the haze from the input image based on the determined peak and valley angles.

Abstract: A method for restoring an image of a blurred barcode includes capturing the image, restoring the image by selecting one Optical Transfer Function (OTF) in a database and performing deconvolution on the image, decoding the barcode in the restored image to return numbers represented by the barcode, and displaying the returned numbers.

Abstract: Systems and methods are provided for providing improved de-noising image content by using directional noise filters to accurately estimate a blur kernel from a noisy blurry image. In one embodiment, an image manipulation application applies multiple directional noise filters to an input image to generate multiple filtered images. Each of the directional noise filters has a different orientation with respect to the input image. The image manipulation application determines multiple two-dimensional blur kernels from the respective filtered images. The image manipulation application generates a two-two-dimensional blur kernel for the input image from the two-dimensional blur kernels for the filtered images. The image manipulation application generates a de-blurred version of the input image by executing a de-blurring algorithm based on the two-dimensional blur kernel for the input image.

Abstract: A focal position is searched for by contrast evaluation method, the focus lens is stopped once it is detected that the focus lens has passed the focal position, and the defocus amount at the stopping position is found. The defocus amount corresponding to the difference between the focal position and the stopping position is subtracted from this defocus amount to compute a correction value for the defocus amount. Consequently, a correction value for the focus detection result by phase-difference detection method can be obtained accurately in a focus detection apparatus capable of automatic focus detection by phase-difference detection method and contrast evaluation method, as well as a method for controlling this apparatus.

Abstract: An image editing method for editing an original image is provided. The original image has a distinct figure and an indistinct figure, which correspond to an in-focus object and an out-of-focus object, respectively. The image editing method includes: obtaining an out-of-focus object distance from the out-of-focus object to a lens; performing an inverse blurring process on the indistinct figure according to the out-of-focus object distance and an optical parameter to obtain a processed figure; and forming a processed image according to the processed figure and the distinct figure.

Abstract: A classifier training system trains a classifier for evaluating image deblurring quality using a set of scored deblurred images. In some embodiments, the classifier training system trains the classifier based on a number of sub-images extracted from the scored deblurred images. An image deblurring system applies a number of different deblurring transformations to a given blurry reference image and uses the classifier trained by the classifier training system to evaluate deblurring quality, thereby finding a highest-quality deblurred image. In some embodiments, the classifier training system trains the classifier in the frequency domain, and the image deblurring system uses the classifier trained by the classifier training system to evaluate deblurring quality in the frequency domain. In some embodiments, the image deblurring system applies the different deblurring transformations iteratively.

Abstract: A method for eliminating image blur includes: detecting the difference in pixel value between two corresponding pixels in two continuous images to generate a difference value; and adjusting the luminance of the two corresponding pixels according to the difference value, wherein when the difference value exceeds a predetermined value, the luminance of one pixel of the two corresponding pixels is increased and the luminance of the other pixel is decreased.

Abstract: An image processing apparatus includes: a recognition unit that performs character recognition on an image including at least one character and that obtains a score indicating a recognition accuracy of at least one character region extracted through the character recognition; a local blurring-degree computation unit that computes a first degree of blurring of the at least one character region; and a blurring-degree computation unit that computes a second degree of blurring of the image by using the score and the first degree of blurring of the at least one character region.

Abstract: The invention relates to a method for determining a localization error (?) of a point (P0) of a raw image, comprising the following steps: estimating the value of a statistical magnitude (G) characteristic of a probability law (D(X, Y, Z)) of at least one terrain coordinate (X, Y, Z) associated with the point (P0) of the raw image, using a probability law (D(?1, . . . , ?n)) with magnitudes (?1, . . . , ?n) depending on the exposure conditions of the raw image and a localization function, deduced from an exposure function and a terrain model and applied for the image coordinate point (P0) of the raw image; and deducing the localization error (?) of the point (P0) of the raw image from the statistical magnitude (G).