A method may include: receiving facial image of the patient that depicts the patient's teeth; receiving a 3D model of the patient's teeth; determining color palette of the depiction of the patient's teeth; coding 3D model of the patient's teeth based on attributes of the 3D model; providing the 3D model, the color palette, and the coded 3D model to a neural network; processing the 3D model, the color palette, and the coded 3D model by the neural network to generate a processed image of the patient's teeth; simulating specular highlights on the processed image of the patient's teeth; and inserting the processed image of the patient's teeth into a mouth opening of the facial image.

A method for generating an image processing filter includes: adjusting; and extracting. The adjusting inputs first training image data into a neural network to generate output image data, calculates an evaluation value based on a loss function using the output image data and second training image data, and adjusts a convolution filter so as to reduce the evaluation value. The extracting extracts data from the adjusted convolution filter as data for the image processing filter. A first training image includes noise and reproduces a test pattern. A second training image includes reduced noise and reproduces the test pattern. The loss function includes a first term and a second term. The first term specifies a magnitude of a difference between the output image data and the second training image data. The second term grows smaller as symmetry of the convolution filter relative to a filter axis of symmetry increases.

An image processor comprising a plurality of processing modules configured to transform a raw image into an output image, the modules comprising a first module and a second module, each of which implements a respective trained artificial intelligence model, wherein: the first module is configured to implement an image transformation operation that recovers luminance from the raw image; and the second module is configured to implement an image transformation operation that recovers chrominance from the raw image.

An image processing method and device, a camera apparatus and a storage medium are provided. The image processing method includes: acquiring an initial image, and the initial image corresponds to an RGB data format; dividing the initial image into a plurality of image blocks; converting each image block in the plurality of image blocks from the RGB data format into a YUV data format, wherein Y is a luminance value, and U and V are chromatic value; inverting a luminance value Y of a target image block into a darkness value D, and the target image block is at least one of the plurality of image blocks; coding DUV data of the target image block and YUV data of a non-inverted image block so as to obtain target image data of the initial image.

An image processing apparatus performs predetermined image processing for read data obtained by reading an image on a document by first and second reading sensors arranged so as to have an overlapping area. The predetermined image processing includes color correction processing of converting color signals detected by light receiving elements of the reading sensors into color signals with respect to each of pixels forming read data. Color correction processing for first read data obtained from light receiving elements of the first reading sensor not included in the overlapping area and color correction processing for second read data obtained from light receiving elements of the second reading sensor not included in the overlapping area are performed in accordance with different correction characteristics. By doing this, an image in which image unevenness caused by individual differences of the short reading sensors is reduced can be output.

The invention provides methods that improve image compression and/or quality within the JPEG process by using a low-pass filter to remove high frequency components from image data, which removes blocking artifacts. Preferred embodiments apply the low-pass filter to the Chroma components after decompression prior to conversion into RGB color space.

The color coding approach adopted by the Anchor Point Cloud Compression (PCC) leads to a significant color distortion, even at very low compression ratios. This color distortion is also referred to as color leaking. Two approaches are able to mitigate the color leaking. Both approaches reduce the color leaking significantly, while one of them (e.g., 1D subsampling) is more robust and preferred in most cases. A more general approach is an adaptive selection between these two approaches.

The present application provides an image processing method, an image processing apparatus, an electronic device and a computer-readable storage medium, and relates to the field of image processing technologies. An implementation includes: acquiring an image to be processed; converting the image to be processed into a three-channel YUV image; performing a convolution operation on a Y-channel image, a U-channel image and a V-channel image in the three-channel YUV image to generate an R-channel image, a G-channel image and a B-channel image, respectively, and acquiring a three-channel RGB image; and pre-processing the three-channel RGB image. According to the present application, the image pre-processing speed can be improved.

A method may include: receiving facial image of the patient that depicts the patient's teeth; receiving a 3D model of the patient's teeth; determining color palette of the depiction of the patient's teeth; coding 3D model of the patient's teeth based on attributes of the 3D model; providing the 3D model, the color palette, and the coded 3D model to a neural network; processing the 3D model, the color palette, and the coded 3D model by the neural network to generate a processed image of the patient's teeth; simulating specular highlights on the processed image of the patient's teeth; and inserting the processed image of the patient's teeth into a mouth opening of the facial image.

A medical signal processing device 6 receives a picture signal imaged by a medical observation device 2 and processes the picture signal. The medical signal processing device 6 includes: a signal input unit 61 that receives the picture signal; a first signal converting unit 62 that converts the picture signal received by the signal input unit 61 from an RGB signal into a luminance signal and a color difference signal by performing a matrix calculating process; an identification information appending unit 63 that appends color gamut identification information related to a color gamut of the picture signal and corresponding to the matrix calculating process, to the picture signal resulting from the conversion; and a signal output unit 64 that outputs the picture signal to which the color gamut identification information has been appended, to either a medical display device 4 or a medical recording device provided externally.