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Found 10 papers, 6 papers with code
Toward Tiny and High-quality Facial Makeup with Data Amplify Learning
The core idea of DAL lies in employing a Diffusion-based Data Amplifier (DDA) to "amplify" limited images for the model training, thereby enabling accurate pixel-to-pixel supervision with merely a handful of annotations.
Lightweight network towards real-time image denoising on mobile devices
In this paper, we identify the real bottlenecks that affect the CNN-based models' run-time performance on mobile devices: memory access cost and NPU-incompatible operations, and build the model based on these.
SepLUT: Separable Image-adaptive Lookup Tables for Real-time Image Enhancement
Image-adaptive lookup tables (LUTs) have achieved great success in real-time image enhancement tasks due to their high efficiency for modeling color transforms.
Ranked #5 on
Image Enhancement
on MIT-Adobe 5k
(PSNR on proRGB metric)
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement
They adopt a sub-optimal uniform sampling point allocation, limiting the expressiveness of the learned LUTs since the (tri-)linear interpolation between uniform sampling points in the LUT transform might fail to model local non-linearities of the color transform.
Ranked #2 on
Photo Retouching
on MIT-Adobe 5k
Learning to Extract Flawless Slow Motion From Blurry Videos
To address the temporal smoothness requirement we propose a system with two networks: One, DeblurNet, to predict sharp keyframes and the second, InterpNet, to predict intermediate frames between the generated keyframes.
Normalized Blind Deconvolution
We introduce a family of novel approaches to single-image blind deconvolution, ie , the problem of recovering a sharp image and a blur kernel from a single blurry input.
Learning to Extract a Video Sequence from a Single Motion-Blurred Image
We present a method to extract a video sequence from a single motion-blurred image.
Deep Mean-Shift Priors for Image Restoration
We show that the gradient of our prior corresponds to the mean-shift vector on the natural image distribution.
Ranked #77 on
Image Super-Resolution
on Set14 - 4x upscaling
Noise-Blind Image Deblurring
We present a novel approach to noise-blind deblurring, the problem of deblurring an image with known blur, but unknown noise level.
