Abstract: A system and method for performing a depth estimation procedure includes a camera device with a sensor device for capturing blur images of a photographic target. Each of the captured blur images has a corresponding scene type that includes either a Gaussian scene type or a pillbox scene type. A depth generator performs a scene recognition procedure to identify the appropriate scene type for the respective blur images. The depth generator then selects an effective depth estimation procedure depending upon the detected scene type.

Abstract: A system and method for performing a depth estimation procedure utilizing defocused pillbox images includes a camera device with a sensor device for capturing pillbox blur images of a photographic target. The camera utilizes a depth estimator for performing a Gaussianization procedure that transforms the pillbox blur images into corresponding Gaussian blur images. The Gaussianization procedure is performed by convolving the pillbox blur images with a Gaussianization kernel to generate the corresponding Gaussian blur images. The depth estimator then utilizes the Gaussian blur images for effectively performing the depth estimation procedure.

Abstract: A system and method for generating a direction confidence measure includes a camera sensor device that captures blur images of a photographic target. A depth estimator calculates matching errors for the blur images. The depth estimator then generates the direction confidence measure by utilizing the matching errors and a dynamic optimization constant that is selected depending upon image characteristics of the blur images.

Abstract: An apparatus and method for determining confidence in focus direction detection, including the steps of capturing a plurality of images, calculating sets of matching errors and blur difference estimations relating to the images, capturing a plurality of object images; calculating sets of matching errors and blur difference estimations relating to the images, calculating a confidence indicator as a function of either the matching errors or blur difference estimations, and automatically adjusting a focus control element in response to said confidence indicator exceeding a threshold value.

Abstract: A method and apparatus for performing a blur rendering process on an image is disclosed. In one embodiment, the method of performing a blur rendering process includes accessing a filtered image and depth map information, determining a plurality of blending coefficients for computing a weighted sum for the image and filtered image, wherein the plurality of blending coefficients define a substantially smooth transition from at least one first depth class to at least one second depth class and a substantially sharp transition from the at least one second depth class and the at least one first depth class, wherein the at least one first depth class and the at least one second depth class form at least a portion of a plurality of depth classes and combining the image and the filtered image into a resulting image using the plurality of coefficients.

Abstract: There are many applications that conduct both generation of contrast or complexity level and motion estimation for video processing. The applications often use a block matching technique. An embedded system such as a personal digital camera is an example of such an application. Additionally, comparison of error differences around the location of minimum error in a motion estimation error table is able to be used to determine low contrast in a scene.

Abstract: An apparatus and method for determining confidence in focus direction detection, including the steps of capturing a plurality of images, calculating sets of matching errors and blur difference estimations relating to the images, capturing a plurality of object images; calculating sets of matching errors and blur difference estimations relating to the images, calculating a confidence indicator as a function of either the matching errors or blur difference estimations, and automatically adjusting a focus control element in response to said confidence indicator exceeding a threshold value.

Abstract: A Depth Map (DM) is able to be utilized for many parameter settings involving cameras, camcorders and other devices. Setting parameters on the imaging device includes zoom setting, aperture setting and shutter speed setting.

Abstract: A camera and method which selectively applies image content adjustments to elements contained in the image material. By way of example, the method involves registration of user touch screen input and determination of the arbitrary extent of a specific element in the captured image material at the location at which touch input was registered. Once selected, the element can be highlighted on the display, and additional user input may be optionally input to control what type of adjustment is to be applied. Then the element within the captured image material is processed to apply automatic, or user-selected, adjustments to the content of said element in relation to the remainder of the captured image. The adjustments to the image element may comprise any conventional forms of image editing, such as saturation, white balance, exposure, sizing, noise reduction, sharpening, blurring, deleting and so forth.

Abstract: A method and apparatus for performing a blur rendering process on an image is disclosed. In one embodiment, the method of performing a blur rendering process includes accessing a filtered image and depth map information, determining a plurality of blending coefficients for computing a weighted sum for the image and filtered image, wherein the plurality of blending coefficients define a substantially smooth transition from at least one first depth class to at least one second depth class and a substantially sharp transition from the at least one second depth class and the at least one first depth class, wherein the at least one first depth class and the at least one second depth class form at least a portion of a plurality of depth classes and combining the image and the filtered image into a resulting image using the plurality of coefficients.

Abstract: A method and apparatus for generating a dense depth map. In one embodiment, the method includes applying a joint bilateral filter to a first depth map to generate a second depth map, where at least one filter weight of the joint bilateral filter is adapted based upon content of an image represented by the first depth map, and the second depth map has a higher resolution than the first depth map.

Abstract: A method for high dynamic range compression uses a modified cumulative histogram as a compression curve. This curve is computed from the cumulative histogram of the image with constraints that the local derivative on the curve does not exceed a certain limit. The limit is fixed along the curve or the limit is variable, taking into account noise characteristics at various pixel values. To provide appropriate detail preservation, a smoothing filter is used to separate the image into an illumination image, referred to as a base image, and a detail image, and the compression curve is applied to the base image only. The compression method provides high dynamic range compression of the image while preserving the global contrast perception. Conventional global algorithms for high dynamic range compression are not capable of achieving this result. The proposed high dynamic range compression method also minimizes noise amplification while lightening the dark areas during image compression.

Abstract: A method of automatically detecting and correcting halo artifacts within a processed image is described. The method computes a two-dimensional (2D) gradient field of the original image and a 2D gradient field of the processed image. Each gradient field includes a gradient vector corresponding to each pixel. To detect halo artifacts, the gradient vector at each pixel of the original image is compared to the gradient vector at the corresponding pixel of the processed image. A halo artifact is determined to exist at a given pixel if a direction of the two corresponding gradient vectors differs by at least a specified threshold. To correct the halo artifacts, a composite gradient field is generated using one of three correction methods. A final image is generated by integrating the newly generated composite gradient field using known integration methods from a 2D gradient field such as ones based on the Fast Fourier Transform.

Abstract: A high dynamic range (HDR) compression method and apparatus modeled after the heat equation describing temperature changes in a thin plate. This approach allows combining high dynamic range compression together with noise reduction in a single process, to be performed within the same iteration of the heat equation. Noise reduction is of particular concern while performing HDR compression because brightening of dark areas during high dynamic range compression has the potential to increase noise levels. Performing image processing techniques in combination according to the invention provides enhanced results while lowering the overall processing overhead. This innovation extends the heat equation analogy by adding anisotropic diffusion as an additional term, which allows joint operation of HDR and NR and mitigates noise enhancement within HDR compression during shadow enhancement.

Abstract: A method of evaluating halo artifacts is described herein. The method utilizes a pattern of color patches, a color space and color difference metrics to analyze color changes which correlate to the amount of halo. The pattern of color patches is utilized in the CIE L*a*b* color space to determine an area of patch unaffected by halo of the pattern of color patches. After the area of patch unaffected by halo is determined, a Reference Value is computed by averaging the CIE L*a*b* color for the area of patch unaffected by halo. Then an Artifact Value is calculated either by averaging the CIE L*a*b* color for the area outside the area of patch unaffected by halo but before the margin or by averaging the CIE L*a*b* color on the edge of the patch. Once these values are determined, the halo quantity is calculated.

Abstract: A camera and method which selectively applies image content adjustments to elements contained in the image material. By way of example, the method involves registration of user touch screen input and determination of the arbitrary extent of a specific element in the captured image material at the location at which touch input was registered. Once selected, the element can be highlighted on the display, and additional user input may be optionally input to control what type of adjustment is to be applied. Then the element within the captured image material is processed to apply automatic, or user-selected, adjustments to the content of said element in relation to the remainder of the captured image. The adjustments to the image element may comprise any conventional forms of image editing, such as saturation, white balance, exposure, sizing, noise reduction, sharpening, blurring, deleting and so forth.

Abstract: A method for high dynamic range compression uses a modified cumulative histogram as a compression curve. This curve is computed from the cumulative histogram of the image with constraints that the local derivative on the curve does not exceed a certain limit. The limit is fixed along the curve or the limit is variable, taking into account noise characteristics at various pixel values. To provide appropriate detail preservation, a smoothing filter is used to separate the image into an illumination image, referred to as a base image, and a detail image, and the compression curve is applied to the base image only. The compression method provides high dynamic range compression of the image while preserving the global contrast perception. Conventional global algorithms for high dynamic range compression are not capable of achieving this result. The proposed high dynamic range compression method also minimizes noise amplification while lightening the dark areas during image compression.

Abstract: A method of automatically detecting and correcting halo artifacts within a processed image is described. The method computes a two-dimensional (2D) gradient field of the original image and a 2D gradient field of the processed image. Each gradient field includes a gradient vector corresponding to each pixel. To detect halo artifacts, the gradient vector at each pixel of the original image is compared to the gradient vector at the corresponding pixel of the processed image. A halo artifact is determined to exist at a given pixel if a direction of the two corresponding gradient vectors differs by at least a specified threshold. To correct the halo artifacts, a composite gradient field is generated using one of three correction methods. A final image is generated by integrating the newly generated composite gradient field using known integration methods from a 2D gradient field such as ones based on the Fast Fourier Transform.

Abstract: A method of evaluating halo artifacts is described herein. The method utilizes a pattern of color patches, a color space and color difference metrics to analyze color changes which correlate to the amount of halo. The pattern of color patches is utilized in the CIE L*a*b* color space to determine an area of patch unaffected by halo of the pattern of color patches. After the area of patch unaffected by halo is determined, a Reference Value is computed by averaging the CIE L*a*b* color for the area of patch unaffected by halo. Then an Artifact Value is calculated either by averaging the CIE L*a*b* color for the area outside the area of patch unaffected by halo but before the margin or by averaging the CIE L*a*b* color on the edge of the patch. Once these values are determined, the halo quantity is calculated.

Abstract: An apparatus and method that extend the graduated neutral density filter approach by implementing an in-camera adjustable neutral density filter are described. The adjustable neutral density filter is implemented by the means of a transmissive LCD. The transmissive LCD is controlled to form a mask image. This mask image is able to be computed using an acquired signal wherein the acquired signal is then inverted and blurred. In an embodiment, a splitter and an additional sensor are utilized to acquire a split signal and then modify the split signal for use as the mask image. The other split signal is filtered through the mask image and transmissive LCD. Images with a high dynamic range compression are ultimately captured.