Computer Vision News - October 2021

3 Summary 7 extracted features from the encoder are fed into DISCNet which transforms them into R1, R2, R3. These are then reconstructed back to the final tone-mapped sharp images. The network is fed with condition maps of size H x W x (b+C), where b stands for the kernel code (a b-dimensional vector of the PDF dimensionally reduced by Principal Component Analysis) and H, W, C represent size and channels of the degraded images respectively. Given the condition maps as input, the condition encoder extracts scale-specific feature maps H1, H2, H3 using 3 blocks like the encoder of the restoration branch. This manages to recover saturated information from nearby low-light regions in the degraded images with spatial variability. Then, the extracted features at different scales are fed into their corresponding filter generators , where each comprises a 3 × 3 convolution layer, two residual blocks, and a 1 × 1 convolution layer to expand feature dimension. The predicted filters are output and passed into a dynamic convolution element which finally refines the features and cast them into the main restoration branch. The network is trained on a combination of: • A synthetic dataset, generated from HDR images with large dynamic ranges, from which a degraded image has been simulated using the degradation model defined above and the calibrated PSF. • A real dataset, made of three images of different exposures, taken with a ZTE Axon 20 phone and combined in a unique HDR image. Removing Diffraction Image Artifacts in ...