13 research outputs found
Generalized Expectation Maximization Framework for Blind Image Super Resolution
Learning-based methods for blind single image super resolution (SISR) conduct
the restoration by a learned mapping between high-resolution (HR) images and
their low-resolution (LR) counterparts degraded with arbitrary blur kernels.
However, these methods mostly require an independent step to estimate the blur
kernel, leading to error accumulation between steps. We propose an end-to-end
learning framework for the blind SISR problem, which enables image restoration
within a unified Bayesian framework with either full- or semi-supervision. The
proposed method, namely SREMN, integrates learning techniques into the
generalized expectation-maximization (GEM) algorithm and infers HR images from
the maximum likelihood estimation (MLE). Extensive experiments show the
superiority of the proposed method with comparison to existing work and novelty
in semi-supervised learning
RoughSet-DDPM: An Image Super-Resolution Method Based on Rough set Denoising Diffusion Probability Model
Image super-resolution aims to generate high-resolution (HR) images from low-resolution (LR) inputs. Existing methods like autoregressive models, generative adversarial networks (GANs), and denoising diffusion probability models (DDPMs) have limitations in image quality or sampling efficiency. This paper proposes Rough Set-DDPM, a new super-resolution technique combining rough set theory and DDPMs. The rough set formulation divides the DDPM sampling sequence into optimal sub-columns by minimizing roughness of sample sets. Particle swarm optimization identifies the sub-columns with lowest roughness. Rough Set-DDPM applies iterative denoising on these optimal columns to output HR images. Experiments on the FFHQ dataset validate that Rough Set-DDPM improves DDPM sampling efficiency while maintaining image fidelity. Quantitative results show Rough Set-DDPM requires fewer sampling steps and generates higher quality HR images compared to autoregressive models and GANs. By enhancing DDPM sampling, Rough Set-DDPM provides an effective approach to super-resolution that balances image quality and sampling speed. The key contributions include introducing rough sets to optimize DDPM sampling and demonstrating superior performance over existing methods
PromptIR: Prompting for All-in-One Blind Image Restoration
Image restoration involves recovering a high-quality clean image from its
degraded version. Deep learning-based methods have significantly improved image
restoration performance, however, they have limited generalization ability to
different degradation types and levels. This restricts their real-world
application since it requires training individual models for each specific
degradation and knowing the input degradation type to apply the relevant model.
We present a prompt-based learning approach, PromptIR, for All-In-One image
restoration that can effectively restore images from various types and levels
of degradation. In particular, our method uses prompts to encode
degradation-specific information, which is then used to dynamically guide the
restoration network. This allows our method to generalize to different
degradation types and levels, while still achieving state-of-the-art results on
image denoising, deraining, and dehazing. Overall, PromptIR offers a generic
and efficient plugin module with few lightweight prompts that can be used to
restore images of various types and levels of degradation with no prior
information on the corruptions present in the image. Our code and pretrained
models are available here: https://github.com/va1shn9v/PromptI