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What is pcsgan (perceptual cyclic-synthesized generative adversarial network?

Thus, better objective functions are needed to improve the quality, fine details and realism of the transformed images. A new model for THM/NIR to VIS image transformation called Perceptual Cyclic-Synthesized Generative Adversarial Network (PCSGAN) is introduced to address these issues.

What does pcsgan stand for?

A new method for Thermal (THM)/Near-Infrared (NIR) to Visible (VIS) image transformation called Perceptual Cyclic-Synthesized Generative Adversarial Networks (PCSGAN) is introduced in this paper. The PCSGAN method consists of two different networks, called the generator and the discriminator network like CycleGAN .

How does pcsgan work?

The PCSGAN framework consists two generator networks G V: T → V and G T: V → T to transform the images from Thermal to Visible and Visible to Thermal, respectively. These generator networks are trained adversarially by using two discriminator networks D V and D T in domains V and T, respectively. Fig. 2.

What is pcsgan method?

The PCSGAN method consists of two different networks, called the generator and the discriminator network like CycleGAN . It is like a two player mini-max game, where the discriminator network tries to maximize the given objective function by correctly differentiating between the real and the generated VIS images.

Is pcsgan more efficient than dualgan?

The proposed PCSGAN method consists of an additional residual network for calculating the perceptual loss, which is not present in other compared state-of-the-art methods. The proposed PCSGAN method is memory expensive due to the additional residual network used. However, it is more efficient than the DualGAN model.

What is the difference between pcsgan and ps2gan?

The important points in the analysis are as follows: The proposed PCSGAN uses two Generator and two Discriminator networks similar to DualGAN, CycleGAN and PAN. It is also evident that the No. of parameters in the Generator and Discriminator networks of the proposed PCSGAN are same as CycleGAN and PS2GAN which can be seen as the lowest among all.