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We propose a framework for aligning and fusing multiple images into a single coordinate-based neural representations. Our framework targets burst images that have misalignment due to camera ego motion and small changes in the scene. We describe different strategies for alignment depending on the assumption of the scene motion, namely, perspective planar (i.e., homography), optical flow with minimal scene change, and optical flow with notable occlusion and disocclusion. Our framework effectively combines the multiple inputs into a single neural implicit function without the need for selecting one of the images as a reference frame. We demonstrate how to use this multi-frame fusion framework for various layer separation tasks.
We analyze the spaces of images encoded by generative networks of the BigGAN architecture. We find that generic multiplicative perturbations away from the photo-realistic point often lead to images which appear as artistic renditions of the correspon
We propose a scheme for multi-layer representation of images. The problem is first treated from an information-theoretic viewpoint where we analyze the behavior of different sources of information under a multi-layer data compression framework and co
Multi-focus image fusion (MFIF) has attracted considerable interests due to its numerous applications. While much progress has been made in recent years with efforts on developing various MFIF algorithms, some issues significantly hinder the fair and
This paper presents the Discrete Wavelet based fusion techniques for combining perceptually important image features. SPIHT (Set Partitioning in Hierarchical Trees) algorithm is an efficient method for lossy and lossless coding of fused image. This p
Many real world vision tasks, such as reflection removal from a transparent surface and intrinsic image decomposition, can be modeled as single image layer separation. However, this problem is highly ill-posed, requiring accurately aligned and hard t