English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Medical Image-to-image translation is a key task in computer vision andgenerative artificial intelligence, and it is highly applicable to medicalimage analysis. GAN-based methods are the mainstream image translation methods,but they often ignore the variation and distribution of images in the frequencydomain, or only take simple measures to align high-frequency information, whichcan lead to distortion and low quality of the generated images. To solve theseproblems, we propose a novel method called frequency domain decompositiontranslation (FDDT). This method decomposes the original image into ahigh-frequency component and a low-frequency component, with the high-frequencycomponent containing the details and identity information, and thelow-frequency component containing the style information. Next, thehigh-frequency and low-frequency components of the transformed image arealigned with the transformed results of the high-frequency and low-frequencycomponents of the original image in the same frequency band in the spatialdomain, thus preserving the identity information of the image while destroyingas little stylistic information of the image as possible. We conduct extensiveexperiments on MRI images and natural images with FDDT and several mainstreambaseline models, and we use four evaluation metrics to assess the quality ofthe generated images. Compared with the baseline models, optimally, FDDT canreduce Fr\'echet inception distance by up to 24.4%, structural similarity by upto 4.4%, peak signal-to-noise ratio by up to 5.8%, and mean squared error by upto 31%. Compared with the previous method, optimally, FDDT can reduce Fr\'echetinception distance by up to 23.7%, structural similarity by up to 1.8%, peaksignal-to-noise ratio by up to 6.8%, and mean squared error by up to 31.6%.

Frequency Domain Decomposition Translation for Enhanced Medical Image Translation Using GANs