Image Restoration is a family of inverse problems for obtaining a high quality image from a corrupted input image. Corruption may occur due to the image-capture process (e.g., noise, lens blur), post-processing (e.g., JPEG compression), or photography in non-ideal conditions (e.g., haze, motion blur).
We propose a new PnP scheme, based on the Half-Quadratic Splitting proximal algorithm, combining external and internal priors.
At each stage, we introduce a novel per-pixel adaptive design that leverages in-situ supervised attention to reweight the local features.
Ranked #1 on Image Denoising on SIDD
Compressed sensing (CS) computed tomography has been proven to be important for several clinical applications, such as sparse-view computed tomography (CT), digital tomosynthesis and interior tomography.
Machine learning models are commonly trained end-to-end and in a supervised setting, using paired (input, output) data.
Ranked #1 on Image Denoising on FFHQ
We demonstrate the capabilities of the proposed 3-stage structure with three typical color image restoration tasks: color image demosaicking, color compression artifacts reduction, and real-world color image denoising.
Different from the existing labor-intensive handcrafted architecture design paradigms, we present a novel method, termed as multi-sCaLe nEural ARchitecture sEarch for image Restoration (CLEARER), which is a speciﬁcally designed neural architecture search (NAS) for image restoration.
In contrast to existing works, which mostly operate by latent codes, we discover interpretable directions in the space of the generator parameters.
We also propose a method of estimating scattering parameters, such as airlight, and a scattering coefficient, which are required for our dehazing cost volume.