Abstract: Aspects of the method, apparatus, non-transitory computer readable medium, and system include obtaining first sensor data and second sensor data. The aspects further include processing the first sensor data using a first processing mode to obtain first image data, wherein the first processing mode is based on a first pixel pattern. The aspects further include processing the second sensor data using a second processing mode to obtain second image data, wherein the second processing mode is based on a second pixel pattern different from the first pixel pattern, and generating an image by combining the first image data and the second image data.

Abstract: Synucleinopathies are a group of neurodegenerative diseases including Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). These diseases are characterized by the aggregation and deposition of ?-synuclein (?-syn) in Lewy bodies (LBs) in PD and DLB or as glial cytoplasmic inclusions in MSA. In healthy brains, only ~4% of ?-syn is phosphorylated at Ser129 (pS129-?-syn), whereas >90% pS129-?-syn may be found in LBs. Embodiments of the invention include a duplex assay for both total ?-synuclein and pS129-?-synuclein, which allows measuring both analytes in the same sample, leading to substantial saving in sample volume. The assays can be widely used in methods for detecting pS129-?-syn in biomedical studies including when only a limited volume of sample is available and high sensitivity is required, offering new opportunities for diagnostic biomarkers, monitoring disease progression, and quantifying outcome measures in clinical trials.

Abstract: The present disclosure provides methods, apparatuses, systems, and computer-readable mediums for performing auto-exposure precognition by an apparatus. A method includes obtaining a first frame, predicting a second frame subsequent to the first frame, obtaining image information corresponding to the second frame, extracting auto-exposure information corresponding to the second frame from the image information, and capturing the second frame using the auto-exposure information.

Abstract: An electronic device includes: memory; a color filter array (CFA); an image sensor operatively connected to the CFA, the image sensor being configured to capture a plurality of images respectively having a plurality of measured spectral densities in a plurality of spectral bands; and a processor operatively connected to the at least one memory and the image sensor, wherein the at least one processor is configured to: measure a plurality of crosstalk values in a plurality of pixels and in a plurality of K channels of the plurality of images (the K represents a number of pixels per a color of the CFA), estimate a plurality of spectral-crosstalk values by applying an estimation function to the plurality of crosstalk values, and control to store, in at least one of the memory, the image sensor, or the processor, the estimated plurality of spectral-crosstalk values.

Abstract: Methods and apparatuses for correcting bad image pixels are described. The described sensor-independent image processing techniques leverage one or more dynamic dictionaries of learned filters for bad pixel correction (e.g., where a camera leverages such dictionaries to efficiently identify filters to accurately adjust and correct bad pixel values). For example, a dictionary may store filters that are learned offline (via a self-supervised learning algorithm implemented at a server using known images and ground truth bad pixel correction values). To select a filter for a bad pixel correction operation, a camera may encode an image patch surrounding a bad pixel (into an encoded patch descriptor) and search the dictionary for a matching patch descriptor key. The camera may then apply the filter (value) corresponding to the searched patch descriptor (key) of the dictionary to the image patch to correct the bad pixel and generate a corrected output image.

Abstract: Provided is an apparatus for obtaining an image includes an image sensor and a signal processing unit that includes a demosaicing unit configured to reconstruct a green signal to have a full pixel resolution by using the input image, a sharpening filter unit configured to generate a first image by sharpening the reconstructed green signal for each preset direction, a direction image generation unit which generates a second image by removing a base band and extracting only a detail band, a gray detection unit configured to detect a gray region of the white balance-processed input image, an edge detection unit configured to detect an edge direction of the white balance-processed input image, and a selection unit configured to generate a third image by blending the first image and the second image, based on the detected gray region and the detected edge direction.

Abstract: An electronic device includes: memory; a color filter array (CFA); an image sensor operatively connected to the CFA, the image sensor being configured to capture a plurality of images respectively having a plurality of measured spectral densities in a plurality of spectral bands; and a processor operatively connected to the at least one memory and the image sensor, wherein the at least one processor is configured to: measure a plurality of crosstalk values in a plurality of pixels and in a plurality of K channels of the plurality of images (the K represents a number of pixels per a color of the CFA), estimate a plurality of spectral-crosstalk values by applying an estimation function to the plurality of crosstalk values, and control to store, in at least one of the memory, the image sensor, or the processor, the estimated plurality of spectral-crosstalk values.

Abstract: A pixel array of an image sensor includes a plurality of color filter array (CFA) cells. Each of the plurality of CFA cells includes first, second, third, and fourth CFA blocks. Each of the first and second CFA blocks includes a plurality of pixels to sense light of a first color. Most pixels of the third CFA block are configured to sense the second color and the third CFA block includes at least one first pixel to sense the first light. Most pixels of the fourth CFA block are configured to sense a third color of light and the fourth CFA block includes at least one second green pixel to sense the first light.

Abstract: A time of flight (ToF) sensor includes: a first pixel including a first photogate to receive light reflected by an object and generate a first phase signal, and a second photogate to generate a second phase signal having a phase difference of 180 degrees with respect to the first phase signal; a second pixel including a third photogate to receive the reflected light and generate a third phase signal different from the first phase signal and a fourth photogate to generate a fourth phase signal having a phase difference of 180 degrees with respect to the third phase signal; a first signal output unit to output the first and second phase signals; and a second signal output unit to output the third and fourth phase signals, wherein the first, second, third and fourth photogates output the first to fourth phase signals during a frame period.

Abstract: Aspects of the method, apparatus, non-transitory computer readable medium, and system include obtaining first sensor data and second sensor data. The aspects further include processing the first sensor data using a first processing mode to obtain first image data, wherein the first processing mode is based on a first pixel pattern. The aspects further include processing the second sensor data using a second processing mode to obtain second image data, wherein the second processing mode is based on a second pixel pattern different from the first pixel pattern, and generating an image by combining the first image data and the second image data.

Abstract: In a method for time-of-flight (ToF) based measurement, a scene is illuminated using a ToF light source modulated at a first modulation frequency FMOD(1). While the light is modulated using FMOD(1), depths are measured to respective surface points within the scene, where the surface points are represented by a plurality of respective pixels. At least one statistical distribution parameter is computed for the depths. A second modulation frequency FMOD(2) higher than FMOD(1) is determined based on the at least one statistical distribution parameter. The depths are then re-measured using FMOD(2) to achieve a higher depth accuracy.

Abstract: In various embodiments methods are provided for the treatment or prophylaxis of liposomal storage diseases. In certain embodiments the methods involve administering to a subject in need thereof one or more molecular tweezers that inhibit protein aggregation.

Abstract: In various embodiments methods for the treatment and/or prophylaxis of lipofuscin-related disorders are provided. In certain embodiments the methods involve administration of an effective amount of a molecular tweezers to a subject in need thereof.

Abstract: Provided is an apparatus for obtaining an image includes an image sensor and a signal processing unit that includes a demosaicing unit configured to reconstruct a green signal to have a full pixel resolution by using the input image, a sharpening filter unit configured to generate a first image by sharpening the reconstructed green signal for each preset direction, a direction image generation unit which generates a second image by removing a base band and extracting only a detail band, a gray detection unit configured to detect a gray region of the white balance-processed input image, an edge detection unit configured to detect an edge direction of the white balance-processed input image, and a selection unit configured to generate a third image by blending the first image and the second image, based on the detected gray region and the detected edge direction.

Abstract: A method of controlling power usage in an image signal processor includes measuring an actual percentage of flat pixels in a digital image, adjusting a percentage threshold of flat pixels in the digital image, based on the measured actual percentage of flat pixels in the digital image; and processing the digital image by the image signal processor based on the percentage threshold of flat pixels. The percentage threshold of flat pixels is adjusted to reduce power used by the image signal processor in processing the digital image.

Abstract: Methods and apparatuses for correcting bad image pixels are described. The described sensor-independent image processing techniques leverage one or more dynamic dictionaries of learned filters for bad pixel correction (e.g., where a camera leverages such dictionaries to efficiently identify filters to accurately adjust and correct bad pixel values). For example, a dictionary may store filters that are learned offline (via a self-supervised learning algorithm implemented at a server using known images and ground truth bad pixel correction values). To select a filter for a bad pixel correction operation, a camera may encode an image patch surrounding a bad pixel (into an encoded patch descriptor) and search the dictionary for a matching patch descriptor key. The camera may then apply the filter (value) corresponding to the searched patch descriptor (key) of the dictionary to the image patch to correct the bad pixel and generate a corrected output image.

Abstract: In a method for time-of-flight (ToF) based measurement, a scene is illuminated using a ToF light source modulated at a first modulation frequency FMOD(1). While the light is modulated using FMOD(1), depths are measured to respective surface points within the scene, where the surface points are represented by a plurality of respective pixels. At least one statistical distribution parameter is computed for the depths. A second modulation frequency FMOD(2) higher than FMOD(1) is determined based on the at least one statistical distribution parameter. The depths are then re-measured using FMOD(2) to achieve a higher depth accuracy.

Abstract: In a method for time-of-flight (ToF) based measurement, a scene is illuminated using a ToF light source modulated at a first modulation frequency FMOD(1). While the light is modulated using FMOD(1), depths are measured to respective surface points within the scene, where the surface points are represented by a plurality of respective pixels. At least one statistical distribution parameter is computed for the depths. A second modulation frequency FMOD(2) higher than FMOD(1) is determined based on the at least one statistical distribution parameter. The depths are then re-measured using FMOD(2) to achieve a higher depth accuracy.

Abstract: A method performed with an image sensor having a pixel array. At least one frame of a scene may be obtained using the pixel array. At least one region of interest (ROI) is identified within the frame. Subsequent frames of the scene are obtained, which involves controlling the pixel array to perform high resolution imaging with respect to the at least one ROI and low resolution imaging using analog binning with respect to remaining regions of the frames.

Abstract: A method for time-of-flight (ToF) guided down-up sampling includes calculating a guide function from a full resolution output of a ToF sensor, downsampling the full resolution output by a predetermined scaling factor, calculating a downsampled depth data from the downsampled output and upsampling the downsampled depth data to full resolution using the full resolution guide function.