그림 1. 제안하는 알고리즘의 흐름도 Fig. 1. Flowchart of the proposed algorithm
그림 2. Inception-v3 네트워크 구조도 Fig. 2. The network diagram of Inception-v3
그림 3. 학습 데이터셋 예시 (a) 안개, (b) 실제 비, (c) 합성된 비, (d) 원본 이미지(DIV2K), (e) 모션 블러, (f) 저 화질, (g) 잡음 Fig. 3. Examples in the training dataset (a) haze, (b) rain real, (c) synthetic rain, (d) original image (DIV2K), (e) motion blurred, (f) low resolution, (g) noise
그림 4. 전이 학습 결과 (a) 정확도, (b) 손실 값 (붉은 색: 학습, 파란 색: 검증) Fig. 4. Transfer learning results (a) accuracy, (b) cross entropy (red: train, blue: validation)
그림 5. 개별 복원 알고리즘 수행 결과 (a) 안개 제거, (b) 비 제거, (c) 블러 제거, (d) 초해상도 복원, (e) 잡음 제거 (왼쪽: 입력 영상, 오른쪽: 복원 결과) Fig. 5. Results of the individual restoration algorithms. (a) dehaze, (b) derain, (c) deblur, (d) SR, (e) denoise (left: input, right: output)
그림 6. 복합 열화 영상 복원 결과 (a) 안개+저화질, (b) 안개+모션 블러, (c) 안개+잡음, (d) 비+저화질, (e) 비+모션 블러, (f) 비+잡음 Fig. 6. Restoration results on multi-degradation image (a) haze+LR, (b) haze+MB, (c) haze+N, (d) rain+LR, (e) rain+MB, (f) rain+N
표 2. 복합 열화 영상 분류 정확도 Table 2. Accuracy on the multi-degradation test dataset
표 3. 복합 열화 영상 복원의 PSNR 평균 및 SSIM 평균 Table 3. PSNR average and SSIM average on the multi-degradation test dataset
표 4. 복합 열화 영상 복원의 CNNIQA 계산 결과 Table 4. CNNIQA evaluation results on the multi-degradation test dataset9
표 1. 단일 열화 영상 분류 정확도 Table 1. Accuracy on the single-degradation test dataset
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