An image scaling processor is disclosed comprising a multi-pixel vertical scaler and a multiline horizontal scaler. The multi-pixel vertical scaler includes a plurality of convolvers that convolve a plurality of pixels in parallel with a vertical kernel comprising weights to generate vertically scaled pixels. The multiline horizontal scaler includes horizontal scaling circuits configured to receive vertically scaled pixels from respective line stores, wherein a given horizontal scaling circuit comprises an input configured to receive one vertically scaled pixel at a time, a pipeline of convolver stages configured to, at each stage, convolve vertically scaled pixels with respective horizontal kernel coefficients. The pipeline transfers pixels from a last convolver stage of the pipeline to a second memory, wherein the pixels from the last convolver stage of the pipeline are horizontally and vertically scaled, wherein the pixels stored in the second memory are arranged to be displayed as a scaled image.

An image processing apparatus includes an acquisition unit configured to acquire a plurality of continuous images, an extraction unit configured to extract a difference between two images among the plurality of continuous images, a first image processing unit configured to acquire a third image by removing a difference between portions smaller than a predetermined size from the difference extracted by the extraction unit, and a first combining unit configured to combine the two images by using the third image acquired by the first image processing unit. The image processing apparatus may implement the aforementioned units using a processor(s) that execute(s) a program of instructions stored in memory.

Image data representative of at least a portion of an image comprising first and second sets of pixels associated with first and second colour channels, respectively, is received. It is determined that a first pixel of the first set of pixels is a saturated pixel. First data based on a value of the first pixel of the second set of pixels is obtained for a first image region comprising the first pixel of the first set of pixels and a first pixel of the second set of pixels. Second data based on respective values of second pixels of the first and second sets of pixels are obtained, based on the first data, for a second image region. The second data is processed to generate output data representative of an estimated value associated with the first colour channel for the first pixel.

Described are systems and methods for generating high dynamic range (HDR) images based on image data obtained from different image sensors for use in detecting events and monitoring inventory within a materials handling facility. The different image sensors may be aligned and calibrated and the image data from the sensors may be generated at approximately the same time but at different exposures. The image data may then be preprocessed, matched, aligned, and blended to produce an HDR image that does not include overexposed regions or underexposed regions.

A system and method of processing an image may include capturing an image of a scene. Image data having M-bits per pixel of the image may be generated. Multiple sets of simulated image data of the scene may be generated by applying different simulated exposure times to the generated image data. A processed image may be derived from the sets of simulated image data. The image data having M-bits per pixel may be an HDR image, and the processed image may be an LDR image.

An image processing apparatus comprises an acquisition unit configured to acquire image data, a first tone correction unit configured to estimate transparency for each region of the image data, and correct tone, a second tone correction unit configured to correct tone of the image data using a tone curve, and a control unit configured to control which tone correction unit is to be used to perform tone correction.

Disclosed embodiments provide systems, methods, and computer-readable storage media for enhancing a vehicle identification with preprocessing. The system may comprise memory and processor devices to execute instructions for receiving an image depicting a vehicle. The image may be analyzed and first predicted identity and first confidence value may be determined. The first confidence value may be compared to a predetermined threshold. The processors may further select a processing technique for modifying the image and further analyze the modified image determining a second predicted identity of the vehicle. And a second confidence value may be determined. And the system may further compare the second confidence value to the predetermined threshold to select the first or second predicted identity for transmission to a user.

Implementations of the disclosure provide for methods, systems, and machine-readable media for image processing. In some embodiments, a method for image processing includes: generating, by a processor, a first histogram and a first parameter set for a first image; constructing, by the processor, a model representative of a second histogram of a second image based on an adjustment parameter set and the first parameter set; generating the second histogram based on the model; and generating a second image based on the first image and the second histogram. In some embodiments, the first histogram is a two-dimensional histogram.

There is provided a transmission device including: a transmission unit that transmits a container in a predetermined format including a first component stream that has, as component data, transmission video data obtained by switching between a plurality of types of transmission video data, and a predetermined number of second component streams that have pieces of other component data; and an information insertion unit that inserts dynamic range information of the component data that each of the component streams has into the component streams.

A method for precisely applying chemicals targeted by digital maps developed from remotely sensed data, including: obtaining EOS data through a growing season of a crop growing in a field; processing the EOS data to reflectance values, removing error-inducing effects of atmospheric alteration from the processed EOS data; calculating from the processed EOS data a crop performance index that indicates one or more poor performing area of the field; generating one or more maps of the crop performance index to allow a user to determine whether each of the one or more poor performing areas of the field are due to biological pests instead of topographic or soil constraints in discrete locations of the field; guiding the user to the one or more poor performing areas of the field using the one or more maps to allow the user to scout the one or more poor performing areas of the field to confirm and identify the biological pests; and providing guidance for a chemical application at the one or more poor performing areas that were confirmed as having the biological pests. Other embodiments are provided.