Abstract: Focus detection is to determine whether an image is in focus or not. Focus detection is able to be used for improving camera autofocus performance. Focus detection by using only one feature does not provide enough reliability to distinguish in-focus and slightly out-of-focus images. A focus detection algorithm of combining multiple features used to evaluate sharpness is described herein. A large image data set with in-focus and out-of-focus images is used to develop the focus detector for separating the in-focus images from out-of-focus images. Many features such as iterative blur estimation, FFT linearity, edge percentage, wavelet energy ratio, improved wavelet energy ratio, Chebyshev moment ratio and chromatic aberration features are able to be used to evaluate sharpness and determine big blur images.

Abstract: Focus detection is to determine whether an image is in focus or not. Focus detection is able to be used for improving camera autofocus performance. Focus detection by using only one feature does not provide enough reliability to distinguish in-focus and slightly out-of-focus images. A focus detection algorithm of combining multiple features used to evaluate sharpness is described herein. A large image data set with in-focus and out-of-focus images is used to develop the focus detector for separating the in-focus images from out-of-focus images. Many features such as iterative blur estimation, FFT linearity, edge percentage, wavelet energy ratio, improved wavelet energy ratio, Chebyshev moment ratio and chromatic aberration features are able to be used to evaluate sharpness and determine big blur images.

Abstract: Fast Focus control is to be performed. The imaging apparatus is an imaging apparatus that includes a focus control unit. The focus control unit performs focus control based on the contrast in an image and the processing result of a two-image matching process. The image is an image generated by an imaging unit. The two-image matching process is a process to be performed with the use of two images that are generated by the imaging unit, with the focus lens being located in different positions.

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: 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: Fast Focus control is to be performed. The imaging apparatus is an imaging apparatus that includes a focus control unit. The focus control unit performs focus control based on the contrast in an image and the processing result of a 2-image matching process. The image is an image generated by an imaging unit. The 2-image matching process is a process to be performed with the use of two images that are generated by the imaging unit, with the focus lens being located in different positions.

Abstract: An imaging apparatus includes a control unit. The control unit performs control to set a first mode or a second mode based on a predetermined condition. The first mode is the mode for performing an auto focus process by moving a focus lens based on the contrast in an image generated by an imaging unit. The second mode is the mode for performing an auto focus process by moving the focus lens based on the result of a matching process performed between a first image and a second image that are generated by the imaging unit with the focus lens being located in different positions.

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 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 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: 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.