Computer Vision News - October 2021

2 Summary 8 Experimental results show that the method is effective for removing diffraction image artifacts in UDC systems. Figures below show how the proposed DISCNet successfully suppresses flare and haze effects around highlights and removes most artifacts in nearby saturated images. The results of the proposed method are compared with 4 State-of-the- Art methods: the Wiener Filter, a classical deconvolution algorithm for linear convolution formation which achieves the lowest image quality compared to the other deep learning methods; the SRMDNF method which uses a super-resolution network and cannot adapt to degraded regions caused by highlight sources; the SFTMD network which iteratively connects the kernel code of degradations and, in this application, also leverages the kernel information to solve the non-blind problem (comparable performance but highest computational cost) and finally the DE-UNet method, a Double-Encoder UNet which doesn’t explicitly use the kernel information (blind model). Similarly, comparisons with representative methods are shown for the real images below. The network proposed by the authors manage to remove diffraction image effects, while leaving least artifacts introduced by the camera. Since the ground- truth images are inaccessible, another comparison is made with the camera output of a ZTE phone. Compute Vision Research