Severity-Channel Conditioning: An Injection-Level Study for Consumer-Device Deblurring

Key points

• This paper studies whether a severity descriptor, partly derived from the paired sharp target, helps adaptation on the iPhoneBlur consumer-device benchmark.1
• Under a shared update budget, input concatenation of a severity map is compared with global feature modulation by capture metadata, spatial feature modulation by the severity map, and a tier-contrastive output objective, together with an unconditioned spectral-loss control, all fine-tuned from the same domain-adapted latent-diffusion baseline.1
• For the evaluated model pair, input concatenation improves overall peak signal-to-noise ratio from 31.23 to 31.30 dB, whereas feature modulation, the contrastive objective, and the spectral-loss control do not exceed the baseline.1
• In one exploratory run, zeroing the channel at inference retains most of the gain in peak signal-to-noise ratio but lowers structural similarity below the baseline.1

Sources (1)

• [1]Severity-Channel Conditioning: An Injection-Level Study for Consumer-Device Deblurring
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 10:05 AM

  Motion-deblurring models lose substantial accuracy when transferred from professional cameras to smartphone-derived imagery.

  This paper studies whether a severity descriptor, partly derived from the paired sharp target, helps adaptation on the iPhoneBlur consumer-device benchmark.

Extractive summary: sentences quoted from the sources.