Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing

14 May 2018 Deniz Engin Anıl Genç Hazim Kemal Ekenel

In this paper, we present an end-to-end network, called Cycle-Dehaze, for single image dehazing problem, which does not require pairs of hazy and corresponding ground truth images for training. That is, we train the network by feeding clean and hazy images in an unpaired manner... (read more)

PDF Abstract

Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Image Dehazing O-Haze Cycle-Dehaze PSNR 19.62 # 3
SIMM 0.67 # 2

Methods used in the Paper


METHOD TYPE
Batch Normalization
Normalization
Residual Connection
Skip Connections
PatchGAN
Discriminators
ReLU
Activation Functions
Tanh Activation
Activation Functions
Residual Block
Skip Connection Blocks
Instance Normalization
Normalization
Convolution
Convolutions
Leaky ReLU
Activation Functions
Sigmoid Activation
Activation Functions
GAN Least Squares Loss
Loss Functions
Cycle Consistency Loss
Loss Functions
CycleGAN
Generative Models