An image processing apparatus includes a processor. The processor is configured to execute a program to input, to a learning unit, capture setting information that is used to capture an image before color conversion, a lightness of a background region of the image before the color conversion, and a set of the image before the color conversion and an image after the color conversion, and prepare color conversion characteristics for performing color conversion on an image in accordance with the capture setting information and the lightness of the background region.

A method for evaluating quality of tone-mapping image based on exposure analysis is provided, which explores the exposure properties on each area of the high dynamic range image utilizing the pre-exposure method and divides the high dynamic range image into three parts of an easy overexposed area, an easy underexposed area and an easy natural-exposed area, wherein different quality characteristics are extracted in different areas, which is capable of ensuring that the follow-up quality characteristic extraction is more targeted. The present invention takes the difference of distortion between the tone-mapping image and the conventional image into account, and extracts image characteristics such as the abnormal exposure rate, the underexposed residual energy, the overexposed residual energy and the exposure color index, so as to accurately reflect the quality degradation of the tone-mapping image.

Systems and methods for restoring the appearances of scans of damaged physical documents. Ink bleed is removed and/or ink added to portions of a scanned image based on determining an ink bleed model by analyzing colors of pixels in the scanned image. Gaps in strokes are reconstructed based on analyzing pixel color at multiple angles around individual pixels in the scanned image to determine whether the individual pixels are part of a stroke. The appearance of the scanned image is also enhanced by comparing pixels that are not already close to a background color or ink color with other nearby pixels and, based on the nearby pixels, adjusting colors of the pixels that are not already close to the background color or ink color. These techniques are used individually or in combination to improve the appearance of the scanned image.

An information processing apparatus of the present invention determines whether or not there is a bias in jobs distributed to a plurality of printers to be used in a distributed printing system. In addition, in a case where there is a bias in distributed jobs, the information processing apparatus decides a color correction target to be applied to a printer having a small number of distributed jobs and resulting in an unused state, applies the decided color correction target to the printer having a small number of jobs, to thereby decide a timing of performing color adjustment on the printer having a small number of jobs. The printer having a small number of jobs and resulting in an unused state is subjected to color adjustment at the decided timing.

The present disclosure generally relates to user interfaces. In some examples, the electronic device provides for transitioning between simulated lighting effects. In some examples, the electronic device applies a simulated lighting effect to an image. In some examples, the electronic device provides user interfaces for applying a filter to an image. In some examples, the electronic device provides for a reduced filter interface. In some examples, the electronic device provides a visual aid displayed in a viewfinder.

A method of color image processing for quantizing output includes obtaining an input for an object pixel which is represented by a vector in a first color space. A modified input equal to the input plus a sum of errors from other pixels in a neighborhood of the object pixel is generated. For each color component in the first color space, where corresponding color components of the modified input are located with respect to a preset range is determined. If the modified input's color component is greater than the preset range, then that color component for an output is determined to be on; if less than the preset range, then that color component for the output is determine to be off; and, if within the preset range, then that color component for the output is determined to be unknown. A transformed modified input is mapped to a perceptual color space when any color component of the output is unknown. Colors consistent with color components of the output that have already been determined are also mapped to the perceptual color space. The color in the perceptual color space that lies closest to the transformed modified input is chosen. An output in the first color space having color components on and off is generated consistent with the determinations and/or choices made. Error for the object pixel is then calculated as the difference between the output and the modified input.

Aspects of the present disclosure relate to color correction in image processing pipelines. An example method may include receiving first image data corresponding to reference luminance data and reference chrominance data for each of a plurality of pixels, determining that the first image data corresponds to a raw image captured in a dark environment, generating second image data by performing one or more tone mapping operations on the first image data, the second image data corresponding to current luminance data and current chrominance data for each of the plurality of pixels, and generating output image data. For each pixel of the plurality of pixels, the output data may include an output luminance value of a corresponding pixel of the current luminance data, and chrominance values of the corresponding pixel from a selected one of the reference chrominance data and the current chrominance data, the selection based at least in part on the reference chrominance data and the current chrominance data.

The processing of RGB image data can be optimized by performing optimization operations on the image data when it is converted into the YCbCr color space. First, a raw RGB color space is converted into a YCbCr color space, and raw RGB image data is converted into YCbCr image data using the YCbCr color space. For each Y-layer of the YCbCr image data, a 2D LUT is generated. The YCbCr image data is converted into optimized CbCr image data using the 2D LUTs, and optimized YCbCr image data is generated by blending CbCr image data corresponding to multiple Y-layers. The optimized YCbCr image data is converted into sRGB image data, and a tone curve is applied to the sRGB image data to produce optimized sRGB image data.

For obtaining good quality luminance dynamic range conversion, we describe an image color processing apparatus (200) arranged to transform an input color (R,G,B) of a pixel of an input image (Im_R2) having a first luminance dynamic range into an output color (Rs, Gs, Bs) of a pixel of an output image (Im_res) having a second luminance dynamic range, which first and second dynamic ranges differ in extent by at least a multiplicative factor 1.5, comprising: a maximum calculation unit (201) arranged to calculate the maximum (M) of at least three components of the input color; a brightness mapper (202) arranged to apply a function (F) to the maximum, yielding an output value (F(M)), whereby the function is predetermined having a constraint that the output value for the highest value of the maximum (M) cannot be higher than 1.0; a scaling parameter calculator (203) arranged to calculate a scaling parameter (a) being equal to the output value F(M)) divided by the maximum (M); and a multiplier (204) arranged to multiply the three color components of the input color (R,G,B) by the scaling parameter (a), yielding the color components of the output color, wherein the color processing apparatus (200) comprises at least one component multiplier (303) arranged to multiply a component (B) of the input color with a weight (wB) being a real number yielding a scaled component (Bw) prior to input of that component in the maximum calculation unit (201).

The present disclosure relates to methods and apparatus for globally adjusting luminance and/or chrominance consistency among multiple images. The methods may include sorting a plurality of images in an order, such that: adjacent images at least partially overlap with each other; and the first image and the last image at least partially overlap with each other. The methods may also include determining mean pixel values for overlapping region of the images. The methods may also include determining consistency factors for the images. The methods may also include generating modified mean pixel values by multiplying the mean pixel values by the respective consistency factors. The methods may also include adjusting the consistency factors to reduce differences of the modified mean pixel values in the overlapping regions. The methods may further include adjusting pixel values of the images based on the respective consistency factors.