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Deep Image Prior (DIP) shows that some network architectures naturally biastowards smooth images and resist noises, a phenomenon known as spectral bias.Image denoising is an immediate application of this property. Although DIP hasremoved the requirement of large training sets, it still presents two practicalchallenges for denoising: architectural design and noise-fitting, which areoften intertwined. Existing methods mostly handcraft or search for thearchitecture from a large design space, due to the lack of understanding on howthe architectural choice corresponds to the image. In this study, we analyzefrom a frequency perspective to demonstrate that the unlearnt upsampling is themain driving force behind the denoising phenomenon in DIP. This finding thenleads to strategies for estimating a suitable architecture for every imagewithout a laborious search. Extensive experiments show that the estimatedarchitectures denoise and preserve the textural details better than currentmethods with up to 95% fewer parameters. The under-parameterized nature alsomakes them especially robust to a higher level of noise.

The Devil is in the Upsampling: Architectural Decisions Made Simpler for Denoising with Deep Image Prior