A method of method of processing an image includes: determining estimates of parameters of an auto-regressive, AR, parametric model of noise contained in the image, according to which a current noise pixel is computed as a combination of a linear combination of P previous noise pixels in a causal neighborhood of the current noise pixel weighted by respective AR model linear combination parameters (φ.sub.1, . . . , φ.sub.P) with a generated noise sample corresponding to an additive Gaussian noise of AR model variance parameter (σ), generating a noise template of noise pixels based on the estimated AR model parameters, wherein the noise template is of a predetermined pixel size smaller than the pixel size of the image, determining an estimate (σ.sub.P) of a variance of the noise template, and based on a comparison of the estimated variance (σ.sub.P) with a predetermined threshold (T.sub.σ), correcting the AR model variance parameter (σ).

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.

An image processing apparatus for displaying an image on a display screen, inputs data, specifies at least one of a bright region and a dark region of an image corresponding to the image data, executes, for at least one of the bright region and the dark region, at least one of first chroma processing of increasing a chroma of the bright region, second chroma processing of lowering the chroma of the dark region, first hue processing of making a hue of the bright region close to red, and second hue processing of making the hue of the dark region close to blue in accordance with display dynamic range information corresponding to the display screen, and displays, on the display screen, an image based on corrected image data that has undergone correction.

The present principles relate to a method and a device for reducing noise in a component of a picture. The method includes obtaining a low-pass filtered component by low-pass filtering the component of the picture, for a current pixel in the component of the picture. For at least one current neighboring pixel of the current pixel, computing a distance, relative to the current neighboring pixel, between the value of the current pixel in the low-pass filtered component and the value of the current neighboring pixel in the low-pass filtered component. When the distances relative to the at least one neighboring pixels of the current pixel are lower than a first threshold, modifying the value of the current pixel in said component of the picture according to a linear combination of the value of the current pixel in the component of the picture and the value of the current pixel in the low-pass filtered component. Otherwise the value of the current pixel in the at least one component of the picture remains unchanged.

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 for 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.

Various implementations disclosed herein render virtual content while accounting for air-born particles or lens-based artifacts to improve coherence or to otherwise better match the appearance of real content in the images with which the virtual content is combined.

A method of method of processing an image of a video sequence including a plurality of images, wherein the video sequence is divided into a sequence of consecutive subsets each including one or more consecutive images of the video sequence is provides, which includes: determining, among the consecutive subsets, a current subset that includes the image, upon determining that the image is a first image of the current subset to be processed, determining an analysis window corresponding to the current subset, wherein the analysis window includes a plurality of consecutive images of the video sequence, and includes at least one image of the current subset, and generating processing results for the image by performing a processing on the images of the analysis window.

A method of method of processing an image includes: determining estimates of parameters of an auto-regressive, AR, parametric model of noise contained in the image, according to which a current noise pixel is computed as a combination of a linear combination of P previous noise pixels in a causal neighborhood of the current noise pixel weighted by respective AR model linear combination parameters (φ.sub.1, . . . , φ.sub.P) with a generated noise sample corresponding to an additive Gaussian noise of AR model variance parameter (σ), generating a noise template of noise pixels based on the estimated AR model parameters, wherein the noise template is of a predetermined pixel size smaller than the pixel size of the image, determining an estimate (σ.sub.P) of a variance of the noise template, and based on a comparison of the estimated variance (σ.sub.P) with a predetermined threshold (T.sub.σ), correcting the AR model variance parameter (σ).

Provided are a method and a device that can efficiently generate a training dataset. Object information is associated with a visual marker, a training dataset generation jig that is configured from a base part and a marker is used, said base part being provided with an area that serves as a guide for positioning a target object and said marker being fixed on the base part, the target object is positioned using the area as a guide and in this condition an image group of the entire object including the marker is acquired, the object information that was associated with the visual marker is acquired from the acquired image group, a reconfigured image group is generated from this image group by performing a concealment process on a region corresponding to the visual marker or the training dataset generation jig, a bounding box is set in the reconfigured image group on the basis of the acquired object information, information relating to the bounding box, the object information, and estimated target object position information and posture information are associated with a captured image, and a training dataset for performing object recognition and position/posture estimation for the target object is generated.

A system and method for providing a weather effect in an image includes selecting at least one weather texture image indicating weather, and providing a weather effect in the image by overlapping the selected weather texture image on the image.