SSIM
305 papers with code • 1 benchmarks • 4 datasets
Libraries
Use these libraries to find SSIM models and implementations
O-HAZE
I-HAZE
VIDIT
DRealSR
Most implemented papers
Cloud removal in remote sensing images using generative adversarial networks and SAR-to-optical image translation
Akash-Ramjyothi/Cloud-Removal-with-GAN-Satellite-Image-Processing
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We used the SEN1-2 dataset to train and test both GANs, and we made cloudy images by adding synthetic clouds to optical images.
NBNet: Noise Basis Learning for Image Denoising with Subspace Projection
Subsequently, image denosing can be achieved by selecting corresponding basis of the signal subspace and projecting the input into such space.
Focal Inverse Distance Transform Maps for Crowd Localization
dk-liang/FIDTM
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Most regression-based methods utilize convolution neural networks (CNN) to regress a density map, which can not accurately locate the instance in the extremely dense scene, attributed to two crucial reasons: 1) the density map consists of a series of blurry Gaussian blobs, 2) severe overlaps exist in the dense region of the density map.
Combining Attention Module and Pixel Shuffle for License Plate Super-Resolution
The License Plate Recognition (LPR) field has made impressive advances in the last decade due to novel deep learning approaches combined with the increased availability of training data.
An All-in-One Network for Dehazing and Beyond
This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net).
I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images
inyong37/Vision
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This represents an important advantage of the I-HAZE dataset that allows us to objectively compare the existing image dehazing techniques using traditional image quality metrics such as PSNR and SSIM.
DSSLIC: Deep Semantic Segmentation-based Layered Image Compression
makbari7/DSSLIC
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A compact representation of the input image is also generated and encoded as the first enhancement layer.
Context-adaptive Entropy Model for End-to-end Optimized Image Compression
JooyoungLeeETRI/CA_Entropy_Model
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ICLR 2019
We propose a context-adaptive entropy model for use in end-to-end optimized image compression.
CSGAN: Cyclic-Synthesized Generative Adversarial Networks for Image-to-Image Transformation
Most of the research focuses over the suitable objective function for image-to-image transformation.
CAE-ADMM: Implicit Bitrate Optimization via ADMM-based Pruning in Compressive Autoencoders
We introduce ADMM-pruned Compressive AutoEncoder (CAE-ADMM) that uses Alternative Direction Method of Multipliers (ADMM) to optimize the trade-off between distortion and efficiency of lossy image compression.
