Samsung Electronics Co., Ltd.

Intel Corporation

Transchip, Inc.

Samsung Semiconductor Israel R&d Center (sirc)

German Voronov

Eugene Fainstain

Evgeny Artyomov

Tiberiu Galambos

Yair Elmakias

Miriam Fraenkel

Ben Furman

Alexander Shnayder

Ilia Antsiferov

Jong-Hwa Lee

Michael Slutsky

Jarno Nikkanen

Ju Young Lee

Noam Levy

Gilad Michael

Shai Litvak

Tomer Yanir

Roee Sfaradi

Viktor Ariel

Dmitry Rudoy

Edmond Chalom

Leonid Brailovsky

Harish Essaky Sankaran

Nir Darshan

Or Shimshi

Shlomo Somerstein

Artem Zinevich

Methods, apparatuses and systems may provide for operating a machine learning device by obtaining training image data, conducting an offline prediction analysis of the training image data with respect to one or more real-time parameters of an image capture device, and generating one or more parameter detection models based on the offline prediction analysis. Additionally, methods, apparatuses and systems may provide for operating the image capture device by obtaining a candidate image associated with the image capture device, determining that the candidate image corresponds to a particular type of scene represented in a parameter prediction model, and adjusting one or more real-time parameters of the image capture device based at least in part on one or more parameter values associated with the particular type of scene.

Machine learning of real-time image capture parameters

Techniques related to generating downscaled image or image frame data in a luma chroma separated color space for an image or video pipeline architecture are discussed. Such techniques may include converting input image data to the luma chroma separated color space based on adaptive color coefficients determined based on an illumination indicator associated with the input image data and storing downscaled color converted image data to an input image buffer of the image or video pipeline.

Adaptive linear luma domain video pipeline architecture

In one example embodiment, a method includes determining at least one of integral column sums and integral row sums for pixels of an image and determining at least one of a column-wise sum of pixel values and a row-wise sum of pixel values associated with an area within the image based on at least one of the determined integral column sums and the determined integral row sums corresponding to a plurality of the pixels forming the area.

Methods and systems for object detection based on column-wise and row-wise sums of pixel values

An image processing method includes: receiving image data of a Bayer format comprising red color information, green color information, and blue color information, generating image data of a modified Bayer format by combining the green color information with the red color information and combining the green color information with the blue color information while downscaling the image data of the Bayer format to a target resolution, denoising the image data of the modified Bayer format, and generating RGB image data by demosaicing the denoised image data of the modified Bayer format.

Image processing method, image signal processor, and image processing system including the same

In at least one example embodiment, a method of determining motion in an image includes acquiring pixel data from a plurality of pixels in an image sensor, the plurality of pixels having respective response ranges, the response range of at least a first pixel of the pixels including a linear response range and a logarithmic response range, the plurality of pixels configured to produce pixel data according to light of the image in a scene and the response ranges and determining the motion based on the pixel data generated across the response ranges including the logarithmic response range of the at least first pixel.

Digital imaging systems including image sensors having logarithmic response ranges and methods of determining motion

High quality upscaling and denoising are required in mobile imaging devices that do not contain high quality lenses. Such is also required in order to scale up standard-definition video content for display in high-definition television screens. The disclosed method uses contextual information obtained during upscaling and/or denoising of frames. Relevant correspondences between patches within a frame and between frames, are detected, managed and exploited. The correspondence information is simultaneously used and updated while video frames are being processed. Two approaches may be used: 1. keeping, searching for and updating a database of useful patches, by adding frequently visible similar patches, aggregating high-frequency, low-noise information associated with the similar patches, and removing less-observed patches; 2. Using the high-resolution and noise-reduced information that was collected from earlier video frames, and is expressed by the output of latest processed frame, for upscaling and/or noise-reducing the next processed frame.

Image processor for and method of upscaling and denoising using contextual video information

An image capture device can include an analysis unit that can be configured to determine a refined light model that models a light source based on first and second image signals sampled with first and second exposure times, respectively, and corresponding to first and second rows of pixels. A compensation unit can be coupled to the analysis unit, and can be configured to generate an output image signal by compensating the first and second image signals and the second image signal using compensation gains associated with the refined light model.

Image capture devices configured to generate compensation gains based on an optimum light model and electronic apparatus having the same

A digital image stabilization method includes taking N frames of a target image with an exposure of T, dividing each of the N frames into predetermined blocks, and selecting a reference frame from among the N frames by setting one of the blocks in each of the N frames as a reference region and comparing reference regions of the respective N frames with one another. The method further includes estimating a motion vector of each of the remaining frames based on a reference region of the reference frame, and generating a result frame by combining a weighted frame, which is obtained by mapping a weight calculated based on the estimated motion vector to each block, and a color component frame.

Digital image stabilization method and imaging device using the same

A denoising apparatus comprising an image input unit which receives pixel data including color information of pixels included in a correction target image, a denoising unit which denoises the pixel data by a weight based averaging method, wherein the weight is set to a maximum value when a difference value between a correction target block and a comparison target block in the correction target image is zero, decreases linearly to zero as the difference value increases until it reaches a threshold value, and is set to zero when the difference value is greater than or equal to the threshold value, and an image output unit which outputs the pixel data processed by the denoising unit. The denoising unit assigns a corrected weight value to at least a guaranteed number of comparison target blocks for an impulse block, where an impulse block is a correction target block for which the number of non-zero weight valued comparison target blocks is less than a predetermined guaranteed number.

Denoising apparatus, system and method

Imaging systems and methods for processing images. Various of the imaging systems include a motion detection and/or estimation engine. Information from such a motion engine can be used by one or more of a scene definition engine, a blur reduction engine, an anti-shake engine, and a video compression engine. Various of the methods include processes for accepting motion information from a motion detection and/or estimation engine and performing one or more of the following functions: anti-shake, blur reduction, scene definition, video compression, and power management. In some cases, the various systems and methods can be implemented on a single chip.

Systems and methods for utilizing activity detection information in relation to image processing

Systems, methods and devices related to detecting and transmitting images. Imaging system and devices, as well as methods of using such that are provided herein include flicker detection and/or correction; and/or built-in self test associated with various analog circuitry in the imaging devices; and/or power reduction ability; and/or pixels with charge evacuation functionality; and/or parallel to serial conversion unit and associated serial output interface; and/or other advanced functionality.

CMOS imager for cellular applications and methods of using such

An imaging apparatus for capturing a still or moving picture is disclosed. The imaging apparatus includes a substrate and an aperture. The substrate includes an imaging array and a pixel. The aperture exposes the imaging array to radiation. The pixel is isolated from radiation passing through the aperture. In some embodiments, the radiation gathered by the pixel can be used to improve the still or moving picture captured by the imaging apparatus.

Calibration pixels for image sensor

Systems, methods and devices related to detecting and transmitting images. Imaging systems and devices, as well as methods of using such that are provided herein include flicker detection and/or correction; and/or built-in self test associated with various analog circuitry in the imaging devices; and/or power reduction ability; and/or pixels with charge evacuation functionality; and/or a parallel to serial conversion unit and associated serial output interface; and/or other advanced functionality.

Cmos imager for cellular applications and methods of using such

A method of capturing an image of a scene includes providing an imaging device having an array of pixels. Each pixel is configured to provide a pixel output. The method also includes determining a frequency of light produced by a light source illuminating the scene and capturing an image of the scene using the pixel array. A first capture time for one or more pixels in the array is different from a second capture time of one or more other pixels in the array. The method further includes, for each pixel in the array, determining a relative phase of the light with respect to the capture time of the pixel, determining a compensating gain based on the relative phase of the light and the frequency of the light, and adjusting the pixel output of the pixel in relation to the compensating gain.

Growing community of inventors

Holon, Israel

German Voronov