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A convolutional neural network trained to distinguish between "real" high-resolution images and those "faked" by the generator.

SRGAN uses a Generative Adversarial Network (GAN) architecture to produce photorealistic results. Instead of just minimizing mean squared error (MSE), it uses a "perceptual loss" function that focuses on visual quality rather than pixel-perfect accuracy. 2. Architecture Overview srganzo1.rar

Discuss the trade-off between (Peak Signal-to-Noise Ratio) and Perceptual Quality . While SRGANs might have lower PSNR, they look much better to the human eye. srganzo1.rar

Typically uses a Residual-in-Residual Dense Block (RRDB) or standard residual blocks to learn feature maps. It includes sub-pixel convolution layers to increase image resolution. srganzo1.rar