Denoising ati Deblurring Nẹtiwọki adanwo

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

1. Kini DnCNN ṣe asọtẹlẹ gangan lakoko ikẹkọ?
   • Aṣeku ariwo, eyiti a yọkuro lẹhinna lati titẹ sii
   • Eto ISO gangan kamẹra
   • A ọrọ apejuwe ti awọn ipele
   • Maapu ijinle ti aworan naa
2. Kilode ti ifọju afọju ṣe ka lile ju kiko?
   • Nitori awọn aworan jẹ nigbagbogbo ni awọ
   • Nitori ekuro blur ti n ṣapejuwe bi awọn piksẹli ṣe smeared nigbagbogbo jẹ aimọ
   • Nitori deblurring nilo GPS ifihan agbara
   • Nitori ariwo ko le ṣe afikun sintetiki
3. Bawo ni awọn orisii ikẹkọ fun awọn nẹtiwọọki wọnyi ṣe ṣẹda ni igbagbogbo?
   • Nipa ọwọ-yiya mimọ awọn ẹya
   • Nipa ọlọjẹ fiimu odi
   • Nipa fifi ariwo sintetiki tabi blur kun si awọn fọto nu
   • Nipa gbigbasilẹ ohun lẹgbẹẹ aworan naa
4. Ohun ti faaji ti wa ni commonly lo bi awọn gbara fun atunse aworan?
   • Atunṣe laini ti o rọrun
   • Igi ipinnu
   • Ayẹwo ifisi ọrọ kan
   • A U-Net encoder-decoder
5. Kini ero pataki lẹhin ọna ikẹkọ Noise2Noise?
   • O maapu akiyesi akiyesi alariwo kan si ọkan alariwo miiran, ti ko nilo ibi-afẹde mimọ
   • O nilo awọn aworan ibi-afẹde pipe
   • O ṣiṣẹ nikan lori ọrọ
   • Ko si nẹtiwọọki nkankikan rara
6. Why can adding perceptual or adversarial objectives be a tradeoff in restoration?
   • They make images impossible to display
   • They may improve visual naturalness but can favor plausible detail beyond pixel fidelity
   • They guarantee exact recovery of every pixel
   • They remove the need for training data
7. What is SIDD used for in image restoration research?
   • Measuring telescope mirror alignment
   • Ranking speech transcripts
   • Evaluating denoising on real smartphone camera noise
   • Testing particle-detector triggers
8. What is a risk when a generative restoration model sharpens an image?
   • It always reduces image dimensions
   • It converts every image to grayscale
   • It removes the need to inspect results
   • It may invent plausible detail that was not supported by the measurement