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Found 5 papers, 3 papers with code
Histopathological Image Analysis with Style-Augmented Feature Domain Mixing for Improved Generalization
Style transfer-based data augmentation is an emerging technique that can be used to improve the generalizability of machine learning models for histopathological images.
Self Supervised Low Dose Computed Tomography Image Denoising Using Invertible Network Exploiting Inter Slice Congruence
We have shown the aforementioned is similar to training a neural network to minimize the distance between clean NDCT and noisy LDCT image pairs.
Iterative Gradient Encoding Network with Feature Co-Occurrence Loss for Single Image Reflection Removal
In this study, we proposed an iterative gradient encoding network for single image reflection removal.
Noise Conscious Training of Non Local Neural Network powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising
Next, we moved towards the problem of non-stationarity of CT noise and introduced a new noise aware mean square error loss for LDCT denoising.
Lightweight Modules for Efficient Deep Learning based Image Restoration
We also present and analyse our results highlighting the drawbacks of applying depthwise separable convolutional kernel (a popular method for efficient classification network) for sub-pixel convolution based upsampling (a popular upsampling strategy for low-level vision applications).
