Hybrid Light Field Image Denoising Network using 4D-DCT Separated Transform.

This paper proposes a novel hybrid light field (LF) denoising method which is based on a convolutional neural network (CNN) designed to reflect the characteristic of LF image in both pixel and frequency domains. Noting that the image noise usually has much high-frequency energy, the proposed network is designed to operate in a transform domain in two stages. At the first stage, energy compaction of spatial-angular information of LF image is sought by 4D-DCT separated transform which can achieve better energy compaction than 2D-DCT applied separately in the spatial and angular domain. The transformed LF is decomposed into different frequency components and each frequency component is recovered progressively. Subsequently, we reshape and convert different frequency components into pixel domain to perform the next refinement step for which a residual spatial-angular block (RSAB) is proposed to handle the 4D LF structure in the pixel domain. Extensive experimental results on different noisy datasets confirm the effectiveness of our proposed method compared to state-of-the-art methods in both objective and subjective quality.