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Found 7 papers, 3 papers with code
Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time
The small CNN works on the down-sampled version of the input image to predict content-dependent weights to fuse the multiple basis 3D LUTs into an image-adaptive one, which is employed to transform the color and tone of source images efficiently.
Ranked #5 on
Image Enhancement
on MIT-Adobe 5k
(SSIM on proRGB metric)
Grid Anchor based Image Cropping: A New Benchmark and An Efficient Model
The employed evaluation metrics such as intersection-over-union cannot reliably reflect the real performance of a cropping model, either.
CameraNet: A Two-Stage Framework for Effective Camera ISP Learning
Traditional image signal processing (ISP) pipeline consists of a set of individual image processing components onboard a camera to reconstruct a high-quality sRGB image from the sensor raw data.
Reliable and Efficient Image Cropping: A Grid Anchor based Approach
Consequently, a grid anchor based cropping benchmark is constructed, where all crops of each image are annotated and more reliable evaluation metrics are defined.
Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model
Most of the existing learning-based single image superresolution (SISR) methods are trained and evaluated on simulated datasets, where the low-resolution (LR) images are generated by applying a simple and uniform degradation (i. e., bicubic downsampling) to their high-resolution (HR) counterparts.
Learning a Single Tucker Decomposition Network for Lossy Image Compression with Multiple Bits-Per-Pixel Rates
By changing the rank of the core tensor and its quantization, we can easily adjust the bpp rate of latent image representation within a single CNN.
A Hybrid l1-l0 Layer Decomposition Model for Tone Mapping
State-of-the-art tone mapping algorithms mostly decompose an image into a base layer and a detail layer, and process them accordingly.
