Real-MFF: A Large Realistic Multi-focus Image Dataset with Ground Truth
Multi-focus image fusion, a technique to generate an all-in-focus image from two or more partially-focused source images, can benefit many computer vision tasks. However, currently there is no large and realistic dataset to perform convincing evaluation and comparison of algorithms in multi-focus image fusion. Moreover, it is difficult to train a deep neural network for multi-focus image fusion without a suitable dataset. In this letter, we introduce a large and realistic multi-focus dataset called Real-MFF, which contains 710 pairs of source images with corresponding ground truth images. The dataset is generated by light field images, and both the source images and the ground truth images are realistic. To serve as both a well-established benchmark for existing multi-focus image fusion algorithms and an appropriate training dataset for future development of deep-learning-based methods, the dataset contains a variety of scenes, including buildings, plants, humans, shopping malls, squares and so on. We also evaluate 10 typical multi-focus algorithms on this dataset for the purpose of illustration.
Code (0)
등록된 구현이 없습니다.
Tasks
Multi Focus Image FusionSimilar Papers 제목 키워드 기반
Defocus to focus: Photo-realistic bokeh rendering by fusing defocus and radiance priors
We consider the problem of realistic bokeh rendering from a single all-in-focus image. Bokeh rendering mimics aesthetic shallow depth-of-field (DoF) in professional photography, but these visual effects generated by exis…
HallucinationRealistic Compound-Lens Defocus Blur Synthesis
Defocus blur degrades fine image structures and limits visual perception, which can adversely affect downstream vision tasks. Although recent deep learning deblurring methods have achieved strong performance, their effec…
Image Style Transfer: from Artistic to Photorealistic
The rapid advancement of deep learning has significantly boomed the development of photorealistic style transfer. In this review, we reviewed the development of photorealistic style transfer starting from artistic style …
Deep LearningStyle TransferMRIR: Integrating Multimodal Insights for Diffusion-based Realistic Image Restoration
Realistic image restoration is a crucial task in computer vision, and the use of diffusion-based models for image restoration has garnered significant attention due to their ability to produce realistic results. However,…
DenoisingImage RestorationLanguage ModellingLarge Language Model+1Scaling Out-of-Distribution Detection for Real-World Settings
Detecting out-of-distribution examples is important for safety-critical machine learning applications such as detecting novel biological phenomena and self-driving cars. However, existing research mainly focuses on simpl…
Anomaly SegmentationOut-of-Distribution DetectionSegmentationSelf-Driving Cars+1