Embodiments of the present disclosure generally relate to image signal processing logic, and in particular, to separating an undecimated image signal data to create two components with lower resolution and full-resolution, generating an interpolation guidance information based on the two components created by separation, forming a difference image data representing the difference between the chroma and luma values of each pixel and its neighboring pixels, and merging the processed image data from the processing pipelines with the unprocessed image data using the interpolation guidance information generated. The generation of the interpolation guidance information is based on determining distances between pixel values from a group comprising pixels from interpolation nodes, pixels diagonally located adjacent to the interpolation nodes, pixels horizontally adjacent to the interpolation nodes, and pixels vertically adjacent to the interpolation nodes.

An input circuit writes image data into a line buffer. An output circuit reads pixel data of a pixel to be sampled among the pixel data, the pixel specified correspondingly to a shrinking ratio. Further, with a throughput obtained by multiplying a throughput of the output circuit by a square of an inverse number of the shrinking ratio, the input circuit skips another pixel than the pixel to be sampled among the pixel data of an input block of a predetermined size that extends over lines of an inverse number of the shrinking ratio, and writes the pixel data into the line buffer. Upon writing pixel data of an adjacent input block of the input block in a secondary scanning direction into the line buffer, the output circuit reads from the line buffer the pixel data of the pixel to be sampled and continuously outputs the pixel data.

Systems for image evaluation and dynamic cropping are provided. In some examples, a system, may receive an instrument or image of an instrument. Identifying information may be extracted from the instrument or image of the instrument. Based on the extracted identifying information, a check/check image profile may be retrieved. In some examples, expected size and/or shape data may be extracted from the check/check image profile. The extracted expected size and/or shape data may be compared to size and/or shape data from the received instrument or image of the instrument to identify any anomalies (e.g., to determine whether the expected size and/or shape data matches the size and/or shape data of the received instrument or image of the instrument. If the expected size and/or shape data does not match size and/or shape data from the received instrument or image of the instrument, the instrument or image of the instrument may be programmatically modified and a modified image of the instrument may be generated.

In the described examples, a compiled image scaler includes a set of machine executable instructions that generate a scaled image that is a scaled version of a source image with integer and bitwise operations. The compiled image scaler employs filtering to blend colors of adjacent pixels in the source image to generate the scaled image, and each filtering operation concurrently scales three color channels of a pixel in the source image.

In one embodiment, a method includes determining characteristics of one or more areas in an image by analyzing pixels in the image, computing a sampling density for each of the one or more areas in the image based on the characteristics of the one or more areas, generating samples corresponding to the image by sampling pixels in each of the one or more areas according to the associated sampling density, and providing the samples to a machine-learning model as an input, where the machine-learning model is configured to reconstruct the image by processing the samples.

Imaging systems and methods are disclosed that use decimate image data to create smaller image frame size from the image size acquired from an imaging array. An imaging system includes an array of photodetectors configured to produce an array of intensity values corresponding to light intensity at the photodetectors. The imaging system can be configured to acquire a frame of intensity values, or an image frame, and reduce the size of the image frame for subsequent processing and display. The decimation process includes replacing a subframe or kernel of image date with few pixels than contained in the kernel, including replacing the pixels of the kernel with one decimated pixel. The decimated pixel values are derived from the pixels of the kernel and may also include replacement of bad pixels in the original image frame.

Disclosed herein is a full-screen display device capable of sufficiently securing light transmittance of a sensor area overlapping a sensor unit in a pixel array and minimizing deterioration in perceived image quality of the sensor area. The pixels are arranged in the sensor area overlapping the sensor unit in the pixel array of the full-screen display device such that the number of pixels gradually decreases from the outer periphery toward the center of the sensor area in units of masks, and the area of a transmission portion gradually increases from the outer periphery toward the center of the sensor area in units of masks.

An embodiment method for converting an initial digital image into a converted digital image, electronic chip, system and computer program product are disclosed, the initial digital image comprising a set of pixels, the pixels being associated respectively with colors, the initial digital image being acquired by an acquisition device, and the converted digital image able to be used by a neural network. The embodiment method comprises redimensioning of the initial digital image in order to obtain an intermediate digital image, the redimensioning being carried out by a reduction in the number of pixels of the initial image, modification of a format of one of the pixels of the intermediate digital image in order to obtain a converted digital image, the modification being carried out, after the redimensioning, by increasing the number of bits used to represent the color of the pixel.

A blur correction device includes a processor including hardware, and the processor obtains an object image from an imaging section, sets any one of a first region where a blur correction is not applied and a second region where the blur correction is applied, based on the object image, finds a third region representing a result of the blur correction applied to the second region, and combines the third region and the first region.

An image signal processor is provided. The image signal processor includes a white balancing block which performs white balancing on a raw RGB image of a Bayer pattern received from an image sensor on a kernel basis or in a kernel unit, a green generation block which performs cross-binning on white-balanced G pixel to generate a first green pixel, and adds a high-frequency component to which a preset weight is applied to generate a binned green pixel, a red-blue generation block which generates a U pixel and a V pixel indicating directionality, on the basis of the binned green pixel, a white-balanced R pixel, and a white-balanced B pixel, and merges the binned green pixel to each of the U pixel and the V pixel to generate a binned red pixel and a binned blue pixel and an inverse white balancing block which performs an inverse white balancing on the binned red pixel, the binned green pixel, and the binned blue pixel to output a final binning image.