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

Most image denoising networks apply a single set of static convolutionalkernels across the entire input image. This is sub-optimal for natural images,as they often consist of heterogeneous visual patterns. Dynamic convolutiontries to address this issue by using per-pixel convolution kernels, but thisgreatly increases computational cost. In this work, we present MalleableConvolution (MalleConv), which performs spatial-varying processing with minimalcomputational overhead. MalleConv uses a smaller set of spatially-varyingconvolution kernels, a compromise between static and per-pixel convolutionkernels. These spatially-varying kernels are produced by an efficient predictornetwork running on a downsampled input, making them much more efficient tocompute than per-pixel kernels produced by a full-resolution image, and alsoenlarging the network's receptive field compared with static kernels. Thesekernels are then jointly upsampled and applied to a full-resolution feature mapthrough an efficient on-the-fly slicing operator with minimum memory overhead.To demonstrate the effectiveness of MalleConv, we use it to build an efficientdenoising network we call MalleNet. MalleNet achieves high-quality resultswithout very deep architectures, making it 8.9x faster than the best performingdenoising algorithms while achieving similar visual quality. We also show thata single MalleConv layer added to a standard convolution-based backbone cansignificantly reduce the computational cost or boost image quality at a similarcost. More information is on our project page:\url{https://yifanjiang.net/MalleConv.html}

Fast and High-Quality Image Denoising via Malleable Convolutions