An electronic device may include enhancement circuitry to enhance high resolution image data to improve perceived quality of an image corresponding to the high resolution image data. The enhancement circuitry may include tone detection circuitry to determine one or more tones within the image and apply changes to the high resolution image data based on the one or more tones. The enhancement circuitry may also include example-based improvement circuitry to compare the high resolution image data to low resolution image data and apply changes to the high resolution image data based on differences between sections of the high resolution image data and sections of the low resolution image data. The enhancement circuitry may also include channel processing circuitry to apply the first and second changes to one or more channels of the high resolution image data.

The present application provides an image enhancement method and system, the method includes acquiring an input brightness value, and computing a log average value of the input brightness value; processing the input image based on a Retinex image enhancement algorithm to obtain a first brightness gain value; computing a brightness gain control factor according to the log average value of the input brightness value; computing a second brightness gain value according to the brightness gain control factor and the first brightness gain value; and obtaining an output image by enhancing the input image according to the second brightness gain value. The present application uses a Retinex image enhancement algorithm to enhance the input image, and by using the brightness gain control factor, when the image has low brightness, the first brightness gain value is compressed, and when the image has high brightness, the first brightness gain value is compressed slightly.

A system and method to analyze image data. The image data may be used to assist in determine the presence of a feature in the image. The feature may include a bubble.

Embodiments relate to enhancing local contrast in an image. A bilateral high pass filter generates a high frequency value for each pixel of an input image, based on a convolution using photometric kernel coefficients associated with other pixels around the pixel. A noise control circuit generates a modulated high frequency value for the pixel based on a noise model for the image defining a noise threshold value for modifying the high frequency value. The modulated high frequency value for the pixel is then combined with a pixel value of the pixel to generate an enhanced value for the pixel. Enhanced values for pixels of the image may be generated to provide the local contrast enhancement for the input image.

Disclosed herein are methods and systems for automated detection of shadow artifacts in optical coherence tomography (OCT) and/or OCT angiography (OCTA). The shadow detection includes applying a machine-learning algorithm to the OCT dataset and the OCTA dataset to detect one or more shadow artifacts in the sample. The machine-learning algorithm is trained with first training data from first training samples that include manufactured shadows and no perfusion defects and second training data from second training samples that include perfusion defects and no manufactured shadows. The shadow artifacts in the OCTA dataset and/or OCT dataset may be suppressed to generate a shadow-suppressed OCTA dataset and/or a shadow-suppressed OCT dataset, respectively. Other embodiments may be described and claimed.

A method for determining a local contrast value for a digital image captured by an image sensor in a camera comprising: applying an edge detection algorithm to image data of the digital image, thereby obtaining a data set pertaining to edges in the digital image; calculating, based on said data set, an edge occurrence value for the digital image; estimating, based on image data of the digital image and a noise model of the image sensor, an estimated image sensor noise for the digital image; and computing the local contrast value as a relationship between the edge occurrence value of the digital image and the expected edge occurrence value for the digital image. A method for adjusting a focus setting of a camera using the local contrast value for images captured by the camera.

Disclosed are a white balance calibration method based on skin color data and an image processing apparatus using the same. By using the method and the image processing apparatus, weight allocation of the skin color data in an input image may be adaptively performed to avoid white balance calibration errors due to excessive skin color data. Therefore, the present disclosure can further solve an issue that in video applications, the white balance calibration is easily interfered by a large amount of the skin color data. Moreover, the present disclosure may be adapted to different color temperatures and may output images having colors closer to actual colors.

An image processing method and an image processing device are provided. The image processing method includes the following steps. A light source is projected onto a 3D model via a plurality of projecting locations to generate a plurality of facial shadow pictures. The facial shadow pictures are superimposed to generate a composite shadow picture. An eye-eyebrow location, a nose location and a mouth location in a 2D facial map are obtained by way of analyzing according to the composite shadow picture. A plurality of characteristic points are calculated according to the eye-eyebrow location, the nose location and the mouth location in the 2D facial map.

One or more embodiments of an image enhancement system enable a computing device to generate an enhanced digital image. In particular, a computing device can enhance a digital image including, for example, a photograph of a whiteboard, document, chalkboard, or other object having a uniform background. The computing device can determine modifications to apply to the digital image by minimizing an energy heuristic that both causes pixels of the digital image to change to a uniform color (e.g., white) and preserves gradients from the digital image. The computing device can further generate an enhanced digital image by applying the determined modifications to the digital image.

An image processing apparatus, comprising: a division unit configured to divide first image data having a first dynamic range into a plurality of regions; an obtaining unit configured to obtain distance information indicating a distance from a focal plane in each of the plurality of regions; a determining unit configured to determine a conversion characteristic of each of the plurality of regions based on the distance information; a conversion unit configured to convert each of the plurality of regions into second image data having a second dynamic range smaller than the first dynamic range by using the conversion characteristic determined by the determining unit; and a storage unit configured to store a first conversion characteristic and a second conversion characteristic that can be used for the conversion.