METHOD FOR TRAINING IMAGE ENHANCEMENT MODEL, IMAGE ENHANCEMENT METHOD, AND READABLE MEDIUM

    公开(公告)号:EP4394692A9

    公开(公告)日:2024-08-14

    申请号:EP22874134.4

    申请日:2022-03-16

    IPC分类号: G06T5/00

    摘要: The present disclosure provides a method for training an image enhancement model. The image enhancement model comprises an enhancement module configured to enhance brightness and contrast. The enhancement module comprises convolution branches corresponding to brightness intervals, and is used for inputting pixels of images inputted therein into the corresponding convolution branches according to the brightness intervals to which the pixels belong; convolution is performed by a first convolution unit in each convolution branch; and the images outputted by the convolution branches are merged and then convolved by a second convolution unit. The method comprises: inputting a sample image into the image enhancement model to obtain a result image outputted by the image enhancement model; calculating a loss, the loss comprising an image loss of the result image with respect to a standard image, and a first constraint loss of brightness histogram constraint of the images outputted by the convolution branches with respect to the standard image in each convolution branch; adjusting the enhancement module according to the loss; and if a training end condition is not satisfied, returning to the step of inputting the sample image into the image enhancement model. The present disclosure further provides an image enhancement method and a computer readable medium.

    BLIND IMAGE DENOISING METHOD AND APPARATUS, ELECTRONIC DEVICE, AND STORAGE MEDIUM

    公开(公告)号:EP4372671A1

    公开(公告)日:2024-05-22

    申请号:EP21950008.9

    申请日:2021-12-08

    发明人: SUN, Yue

    IPC分类号: G06T5/00

    摘要: Provided are a blind image denoising method and apparatus, an electronic device, and a storage medium. The blind image denoising method includes the following: A target noise parameter of a to-be-denoised image is determined according to an image noise calibration result obtained by pre-performing an image noise calibration on an image acquisition device of the to-be-denoised image; a preliminary filtering process is performed on the to-be-denoised image so that a preliminary filtered image of the to-be-denoised image is obtained; a noise level estimation result of the to-be-denoised image is determined according to the target noise parameter and the preliminary filtered image; and a final denoising process is performed on the to-be-denoised image according to the noise level estimation result so that a final blind denoising result of the to-be-denoised image is obtained.