Super Resolution (SRCNN, ESRGAN) — MCQs | Digital Image Processing

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1. Which of the following is the primary goal of Super Resolution in image processing?



2. What type of learning does SRCNN use to perform super-resolution?



3. What is the role of convolutional layers in SRCNN?



4. Which of the following is a limitation of SRCNN?



5. Which model introduced adversarial loss for super resolution?



6. What is the function of the discriminator in ESRGAN?



7. Which of the following interpolation techniques is commonly used for initial upscaling in SRCNN?



8. What does the ‘Residual-in-Residual Dense Block’ (RRDB) improve in ESRGAN?



9. What kind of loss is used in ESRGAN to ensure perceptual quality?



10. Which dataset is commonly used to train super-resolution networks?



11. Which layer in SRCNN is responsible for reconstructing the high-resolution image?



12. What is the effect of using a deeper network in super-resolution tasks?



13. Which performance metric is typically used to evaluate super-resolution results?



14. Which GAN-based architecture is designed specifically for photo-realistic image enhancement?



15. What distinguishes ESRGAN from SRGAN?



16. In the context of super resolution, what does PSNR stand for?



17. Why is perceptual loss important in ESRGAN?



18. Which layer operation is avoided in ESRGAN to preserve texture?



19. What is the primary drawback of using only MSE loss in super-resolution tasks?



20. Which model uses perceptual loss calculated using VGG features?



21. What is the input to the SRCNN model?



22. What does the term ‘deep’ in deep learning refer to in super-resolution networks?



23. Why is bicubic interpolation often used before feeding images to SRCNN?



24. Which component of GAN learns to generate images?



25. What is the purpose of using skip connections in super-resolution networks?



26. Which domain are CNN-based super-resolution models primarily applied in?



27. Which of the following super-resolution models is the most basic CNN-based method?



28. What does FSRCNN stand for?



29. Why is FSRCNN faster than SRCNN?



30. What advantage does ESRGAN have over traditional upsampling techniques?



31. Which layer in ESRGAN helps to avoid vanishing gradients?



32. What is a typical size for training patches in super-resolution networks?



33. Which of the following is not an evaluation metric for super-resolution?



34. What is SSIM used to measure in super-resolution results?



35. Which technique helps avoid checkerboard artifacts during upsampling?



36. Why is VGG network used in perceptual loss?



37. Which of the following is true about adversarial loss in ESRGAN?



38. What does the term “hallucination” refer to in image super-resolution?



39. What is a challenge of using GANs in super-resolution?



40. What is the main contribution of SRGAN?



41. Which method is most suitable for real-time applications among these?



42. Why do deeper networks tend to perform better in super-resolution?



43. Which technique helps ESRGAN generate sharper textures?



44. What is the main reason for using residual learning in super-resolution networks?



45. Which method was the first to introduce deep learning for super resolution?



46. Which model uses pixel shuffle operation for upsampling?



47. What is LPIPS used to evaluate?



48. Which type of noise affects high-resolution image outputs most significantly?



49. What is the primary input of a discriminator in ESRGAN?



50. Which component of ESRGAN primarily contributes to generating fine image details and textures?



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